Search Results for "linux os super"
Sort By:
Showing 7 open source projects for "linux os super"
View related business solutions-
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
-
Forever Free Full-Stack Observability | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
1
MODICIA O.S.
Professional Audio/Video/Graphics
"ISO Unix Linux OS Distribution This distribution comes with all the optimizations, customizations, and exclusive configurations already in place, allowing you to use it right out of the box. You'll have everything you need to work immediately, without complicated setups, and with the best professional software already installed and optimized. -
2
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GUI was designed to provide a stunning experience powered by state-of-the-art technologies without feeling clunky and outdated like other alternatives.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... -
3
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... -
4
VSGAN
VapourSynth Single Image Super-Resolution Generative Adversarial
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.,... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
5
VidCutter
A modern yet simple multi-platform video cutter and joiner
A modern, simple to use, constantly evolving and hella fast MEDIA CUTTER + JOINER w/ frame-accurate SmartCut technology, chapter support, media stream selection for audio + subtitle channels and blackdetect video filter support to automatically detect scene changes or skip commercials in digital TV recordings. Chapter support allows scene chapter names to be included in final media metadata. NOTE: results will only work in media players that support chapters. Flatpak release includes the... -
6
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... -
7
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...
- Previous
- You're on page 1
- Next
