Average Model Cost: $0.0048
Number of Runs: 4,843,407
Models by this creator
Realesrgan is an image restoration model that can be used for both general and anime images. It is designed to enhance the quality and details of low-resolution or noisy images. The model utilizes a deep neural network to learn the mapping between degraded and high-resolution images. It has been trained on a large dataset of images and is capable of producing realistic and visually pleasing results. Realesrgan can be used for various applications such as image super-resolution, denoising, and artifact removal.
GFPGAN is a practical face restoration algorithm that is capable of restoring old photographs or AI-generated faces. It uses a generative adversarial network (GAN) to achieve this task. GANs are deep learning models that consist of two networks: a generator and a discriminator. The generator network is trained to generate new images that resemble the input images, while the discriminator network is trained to differentiate between real and generated images. By iteratively training these two networks, GFPGAN is able to generate high-quality photo-realistic restorations of old photos or AI-generated faces.
ESRGAN is an image super-resolution model that can enhance the resolution of images by a factor of 4x. It is based on a generative adversarial network (GAN) architecture and utilizes a combination of a generator and a discriminator to improve the quality of low-resolution images. The generator network is trained to produce high-resolution images, while the discriminator network is trained to distinguish between the generated images and real high-resolution images. The model has been trained on a large dataset of images and has shown promising results in terms of generating realistic and high-quality super-resolution images.