zsyoaoa / invsr

Arbitrary-steps Image Super-resolution via Diffusion Inversion

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InvSR Model Card

This model card focuses on the models associated with the InvSR project, which is available here.

Model Details

  • Developed by: Zongsheng Yue
  • Model type: Arbitrary-steps Image Super-resolution via Diffusion Inversion
  • Model Description: This is the model used in Paper.
  • Resources for more information: GitHub Repository.

  • Cite as:

    @article{yue2024invSR, author = {Zongsheng Yue, Kang Liao, Chen Change Loy}, title = {Arbitrary-steps Image Super-resolution via Diffusion Inversion}, journal = {arXiv preprint arXiv:2412.09013}, year = {2024}, }

Limitations

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

Training

Training Data The model developer used the following dataset for training the model:

  • Our model is finetuned on LSDIR + 20K samples from FFHQ datasets.

Training Procedure InvSR achieves the goal of image super-resolution via diffusion inversion technique on SD-Turbo, detailed training pipelines can be found in our GitHub repo.

We currently provide the following checkpoints:

Evaluation Results

See Paper for details.