Search Results for "web-based"

A lossless video/GIF/image upscale achieved with waifu2x, Anime4K, SRMD and RealSR. Started in Hack the Valley 2, 2018. The latest Windows update is built based on version 4.8.1. GUI is not available for 5.0.0 yet, but is already under development. Go to the GUI page to see the basic usage of the GUI. Try the mirror if you can't download releases directly from GitHub. You can use Video2X on Google Colab for free if you don't have a powerful GPU of your own. You can borrow a powerful GPU (Tesla K80, T4, P4, or P100) on Google's server for free for a maximum of 12 hours per session. ...

ComfyUI-SeedVR2 Video Upscaler is an open-source integration node for the ComfyUI workflow environment that brings the advanced SeedVR2 video upscaling and restoration model directly into visual AI pipelines. This project packages the SeedVR2 architecture as a custom node for ComfyUI, letting users upscale low-resolution video or imagery inside a node-based interface without needing to write code manually. The underlying SeedVR2 model is known for delivering high-quality video enhancement with strong temporal consistency and improved detail preservation by using diffusion-based techniques that are trained specifically on video sequences. Within the ComfyUI ecosystem, the upscaler integrates with existing nodes and pipelines, making it easier to combine with other processing steps such as denoising, color correction, or format conversion. ...

A collaboration texture pack for Super Mario 64 with the people over at the OldSchool HD, now known as Render 96, that is made of a compillation of the best results ESRGAN upscaling could make as the base for this project, as well of some of the original textures Nintendo used, and a heavier focus in original textures made by project contributors which will eventually be an overwhelming majority of what this project is made of. The end goal for this project is to use the original sources for...

...Anime4K is a simple high-quality anime upscale algorithm. Version 0.9 does not use any machine learning approaches and can be very fast in real-time processing or pretreatment. ACNet is a CNN-based anime upscale algorithm. It aims to provide both high-quality and high-performance. HDN mode can better denoise, HDN level is from 1 to 3, higher for better denoising but may cause blur and lack of detail. Cross-platform, building have already tested in Windows, Linux, and macOS.

Photo/Video/GIF enlargement and Video frame interpolation using machine learning. Waifu2x-Extension-GUI is a video, image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, Real-CUGAN, RTX Video Super Resolution VSR, SRMD, RealSR, Anime4K, RIFE, IFRNet, CAIN, DAIN, and ACNet. The beta build has a faster update cycle than the stable build, which allows you to experience the latest features of the software in advance. Beta builds are...

Sharpen your low-resolution pictures with the power of AI upscaling. SuperImage is a neural network-based image upscaling build with the MNN deep learning framework and the Real-ESRGAN algorithm. By leveraging the power of your device's GPU, SuperImage is able to upscale and restore the details of your images without uploading them to the internet, keeping your data secure. SuperImage is a neural network-based image upscaling application for Android built with the MNN deep learning framework and Real-ESRGAN. ...

REVE (Real-ESRGAN Video Enhance) is a small, fast application written in Rust that is used for upscaling animated video content. It utilizes Real-ESRGAN-can-Vulkan, FFmpeg and MediaInfo under the hood. REVE employs a segment-based approach to video upscaling, allowing it to simultaneously upscale and encode videos. This results in a notable enhancement in performance and enables the feature of reusability. You can download Windows executable file for Intel/AMD/Nvidia GPU. This executable file is portable and includes all the binaries and models required. No CUDA or PyTorch environment is needed.

Single Image Super-Resolution Generative Adversarial Network (GAN) which uses the VapourSynth processing framework to handle input and output image data. Transform, Filter, or Enhance your input video, or the VSGAN result with VapourSynth, a Script-based NLE. You can chain models or re-run the model twice-over (or more). Have low VRAM? Don’t worry! The Network will be applied in quadrants of the image to reduce up-front VRAM usage. You can use any RGB video input, including float32 (e.g., RGBS) inputs. Using VapourSynth you can pass a Video directly to VSGAN, without any frame extraction needed. ...

Super-resolution using a CNN, based on the work of the DGtal team. First of all, an Nvidia graphics card (neither AMD nor Intel integrated) is highly recommended to parallelize the CNN. You will then need to install CUDA. No CUDA = dozens of times slower. This program will generate "model_epoch_ .pth" files corresponding to the model at epoch n, in a folder saved_model_u t_bs bs_tbs tbs_lr lr, where corresponds to the scale factor, bsthe size of the training batch, tbsthe size of the test batch and lrto the learning rate. ...