Inv S R

Introduction

The InvSR model is designed for arbitrary-steps image super-resolution using diffusion inversion techniques. Developed by Zongsheng Yue and featured in the paper "Arbitrary-steps Image Super-resolution via Diffusion Inversion," this model is available on GitHub and offers a novel approach to enhancing image resolution.

Architecture

InvSR utilizes a diffusion inversion method to achieve image super-resolution. It is based on a pre-trained SD-Turbo model, focusing on generating high-resolution images with improved detail. The model requires tiled operations, which can increase inference time and sometimes may not maintain complete fidelity or generate perfect details in complex scenarios.

Training

The model is fine-tuned using the LSDIR dataset along with 20,000 samples from the FFHQ datasets. The training procedure leverages the diffusion inversion technique on the SD-Turbo framework. Details about the training pipelines are available in the GitHub repository. A key checkpoint provided is the noise_predictor_sd_turbo_v5.pth, which is a noise estimation network trained for SD-Turbo.

Guide: Running Locally

1. Clone the Repository: Obtain the source code from the GitHub repository.
2. Install Dependencies: Ensure all necessary libraries and dependencies are installed as specified in the repository's documentation.
3. Download Checkpoints: Obtain the noise_predictor_sd_turbo_v5.pth from the model card to use the pre-trained model.
4. Run the Model: Use the provided scripts to run the model for image super-resolution tasks.

For optimal performance, it is recommended to use cloud GPUs like those offered by AWS, Google Cloud, or Azure.

License

The model is released under an unspecified license. For details, refer to the license link provided in the model card.