Search Results for "model-builder"

...You can also download the flatpak version and double-click the flatpak file to install via Store but wait for the full release, we'll be pushing it to Flathub for easy access. Upscayl uses AI models to enhance your images by guessing what the details could be. It uses Real-ESRGAN (and more in the future) model to achieve this. The CLI tool is called real-esrgan-ncnn-vulkan and it's available on the Real-ESRGAN repository.

...The project is intended as a free and open alternative to commercial AI upscaling tools, allowing developers and digital artists to run the technology locally or integrate it into their own workflows. The repository includes a full application environment with scripts, configuration files, and model support that allow users to run the upscaler as a standalone tool or integrate it into other pipelines.

...The Real-ESRGAN Anime upscaler is much faster and gives better results than waifu2x, so it is recommended to use it instead. Real-ESRGAN only supports scale factors between 2-4, and all other waifu2x options are ignored. By setting the scale factor to 4x, it will use the slower Anime4x model but this gives better results, so it is recommended to upscale images at 4x when using Real-ESRGAN. Apply effects such as speed or reverse (animated images/videos). Customize settings (noise, scale, mode, framerate, etc.). Upscale multiple images (or multiple frames of an animated image/video) concurrently. Change the upscaler (waifu2x or Real-ESRGAN Anime).

This is an ESRGAN model trained specifically for upscaling GameCube and Wii textures, but it can of course be used for other textures from that period, like PlayStation 2, Xbox or PC games from that time. It can be used for all image formats supported by Gamecube and Wii hardware and can remove its typical artifacts like CMPR Block Compression (DXT1 algorithm, also known as BC1), color palette errors, color reduction up to 8bit color depth and 1bit alpha depth.

...Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few commands. When training your own model, start with only PSNR loss (50+ epochs, depending on the dataset) and only then introduce GANS and feature loss. This can be controlled by the loss weights argument. The weights used to produce these images are available directly when creating the model object. ISR is compatible with Python 3.6 and is distributed under the Apache 2.0 license.

Single-Image Super-Resolution for Anime-Style Art using Deep Convolutional Neural Networks. And it supports photo. You can train your own model, change image size, reduce image noise, upscale and customize your image's style. It provides the option of converting and downloading your edited images.