Search Results for "gnu-darwin"
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Showing 8 open source projects for "gnu-darwin"
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Video2X
A lossless video/GIF/image upscaler achieved with waifu2x, Anime4K
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... -
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OptiScaler
OptiScaler bridges upscaling/frame gen across GPUs
OptiScaler is an open-source modding tool designed to enhance graphics performance and flexibility in modern PC games by allowing users to replace or modify built-in upscaling technologies. Instead of relying on the upscaling method originally integrated by a game developer, the software intercepts the game’s rendering pipeline and redirects it to alternative technologies chosen by the user. This makes it possible to swap technologies such as NVIDIA DLSS, AMD FSR, or Intel XeSS even if the... -
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RENDER96-HD-TEXTURE-PACK
Texture pack for Super Mario 64
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... -
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Real-ESRGAN GUI
Cross-platform GUI for image upscaler Real-ESRGAN
The graphic interface of Real-ESRGAN, a practical and beautiful image magnification tool, refers to the design of waifu2x-caffe. This program is a graphical interface of Real-ESRGAN's command line program Real-ESRGAN-ncnn-vulkan , written in Python and tkinter, and supports Windows, Ubuntu and macOS platforms. According to actual measurements, arm64the single-architecture performance is better than universal2the dual- architecture Mac on the Apple chip, so Apple chip users are advised to... -
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AI Upscaler for Blender
AI Upscaler for Blender using Real-ESRGAN
Blender add-on to dramatically reduce render times using the Real-ESRGAN upscaler. Rendering an HD image in Blender takes 37 minutes. Upscaling can render a similar quality image in 5 mins total. Any PC or laptop can now do 3D rendering. 4k images can be rendered in the time it would take to render HD 1080p images. HD 1080p images can be rendered in record time on low-end hardware. Installation is easy. Just install the addon. No special hardware or GPU is required. Upscaling is done... -
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enhancr
Video Frame Interpolation & Super Resolution using NVIDIA's TensorRT
enhancr is an elegant and easy to use GUI for Video Frame Interpolation and Video Upscaling which takes advantage of artificial intelligence - built using node.js and Electron. It was created to enhance the user experience for anyone interested in enhancing video footage using artificial intelligence. The GUI was designed to provide a stunning experience powered by state-of-the-art technologies without feeling clunky and outdated like other alternatives. It features blazing-fast TensorRT... -
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AviSynth AiUpscale v1.2.0
AviSynth+ implementation of Super-Resolution Convolutional Neural
An AviSynth+ implementation of some Super-Resolution Convolutional Neural Networks. Independent luma/chroma upscaling and chroma resampling. Two modes for Video/Photography and Line Art. KrigBilateral option for chroma upscaling/resampling. mpv user shaders of all models for real-time upscaling. Copy AiUpscale.avsi and the Shaders folder to the AviSynth+ plugins folder. The low resolution images were generated using the bicubic filter with Catmull-Rom settings, which is the method commonly... -
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Super-résolution via CNN
Super resolution using a CNN, based on the work of the DGtal team
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...
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