AuraSR, a colossal upscaling model with 600 million parameters, derived from the GigaGAN paper, is now fully open-source. The remarkable feature of this model is its ability to enlarge images fourfold while replenishing details that might be lost during the enlargement process. Moreover, this is not all it can do; it can even upscale images multiple times, enriching the details even further.
From public demonstrations and user feedback, AuraSR performs exceptionally well, with processing speeds that are quite satisfactory. Notably, it not only handles realistic images but also excels with non-realistic content.
As a super-resolution image enhancement model based on Generative Adversarial Networks (GAN), AuraSR is a variant of the GigaGAN paper, focusing on enhancing the resolution of generated images. Currently, it has an implementation version based on Torch, which is derived from the unofficial lucidrains/gigagan-pytorch repository.
Using AuraSR is straightforward, requiring just a few lines of code. First, you need to import the AuraSR module, then create an AuraSR instance from a pre-trained model. Next, you can use the load_image_from_url function to load an image from a URL and resize it appropriately. Finally, call the upscale_4x method to achieve a fourfold enlargement of the image.
The design philosophy of AuraSR is to provide a simple yet effective way to enhance image resolution, making images clearer and more detailed. It can handle natural landscapes, portraits, and even art pieces, improving the overall visual experience.
In summary, AuraSR is an exciting advancement in the field of artificial intelligence, representing cutting-edge technology and promoting the democratization of AI. Through open-source and open science, AuraSR is helping to drive the entire technology sector forward.
Model URL: https://top.aibase.com/tool/aurasr
Online Experience URL: https://fal.ai/models/fal-ai/aura-sr/playground