Search Results for "cuda gpu"
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Showing 27 open source projects for "cuda gpu"
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Taichi
Productive, portable, and performant GPU programming in Python
Taichi is an open-source, embedded DSL within Python designed for high-performance numerical and physical simulations. It uses JIT compilation (via LLVM and its runtime TiRT) to offload compute-heavy code to CPUs, GPUs, mobile devices, and embedded systems. With built-in support for sparse data structures (SNode), automatic differentiation, AOT deployment, and compatibility with CUDA, Vulkan, Metal, and OpenGL ES, it empowers disciplines like simulation, graphics, AI, and robotics -
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rembg
Rembg is a tool to remove images background
Rembg is a powerful tool that utilizes AI (specifically U^2-Net) to automatically remove backgrounds from images, offering a streamlined command-line interface and Docker support. It's ideal for batch processing and integrates smoothly into workflows -
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Anime4KCPP
A high performance anime upscaler
Anime4KCPP provides an optimized bloc97's Anime4K algorithm version 0.9, and it also provides its own CNN algorithm ACNet, it provides a variety of way to use, including preprocessing and real-time playback, it aims to be a high-performance tool to process both image and video. This project is for learning and the exploration task of the algorithm course in SWJTU. Anime4K is a simple high-quality anime upscale algorithm. Version 0.9 does not use any machine learning approaches and can be... -
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ArrayFire
ArrayFire, a general purpose GPU library
ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices. The library serves users in every technical computing market. Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware. The community of ArrayFire developers invites you to build with us if... -
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Warlock-Studio
AI Suite for upscaling, interpolating & restoring images/videos
v6.0. Warlock-Studio is a Windows application that uses Real-ESRGAN, BSRGAN, IRCNN, GFPGAN, RealESRNet, RealESRAnime and RIFE Artificial Intelligence models to upscale, restore faces, interpolate frames and reduce noise in images and videos. the application supports GPU acceleration (including multi-GPU setups) and offers batch processing for large workloads. It includes drag-and-drop handling for single or multiple files, optional pre-resize functions, and an automatic tiling system... -
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Imaging Instruments
High-performance image processing C++ application for Windows Desktop
Imaging Instruments is a high-performance image processing application crafted for efficiency and designed to deliver powerful and reliable image manipulation capabilities. Developed using the Model-View-Controller (MVC) architecture, Imaging Instruments harnesses the full potential of modern technologies, combining C++, Qt6, CUDA-C, and OpenCV to deliver a powerful and seamless experience for both professionals and enthusiasts. With multi-threading support via OpenMP and GPU acceleration through CUDA-C, Imaging Instruments provides lightning-fast image processing, unlocking the full capabilities of modern hardware. Built and designed by Agustin Tortolero. ... -
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CIRT
CIRT - CUDA Interactive Ray Tracer
CIRT is an implementation of PRTP (Programmable Ray Tracing Pipeline). Mainly it is to be used as a ray-tracing equivalent of OpenGL. It allows the user to implement various ray-tracing related algorithms. -
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Imaging Instruments Lite
Image processing App for Windows Desktop
Imaging Instruments lite is a comprehensive image processing application developed following the Model-View-Controller (MVC) design pattern, utilizing Python, Tkinter, and OpenCV. It provides users with image manipulation capabilities, leveraging multi-threading with OpenMP and GPU acceleration using CUDA-C. Fueled by yerba mate and a passion for coding. Created by Agustin Tortolero. website: https://agustintortolero.pythonanywhere.com/ Source code: https://github.com/agustinTortolero/Imaging-Instruments-lite -
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A2M — Audio to MIDI
A2M is a desktop app that converts AUDIO TO MIDI in one click.
A2M (Audio To MIDI) is a simple desktop tool for transcribing local audio files into MIDI files with one click. It is designed primarily for piano recording transcription, and works best on solo piano recordings. Using A2M is straightforward: Select an audio file, click Convert, and the application generates a MIDI file automatically in your Downloads/A2M folder. All processing is done locally on your device, no uploads, no accounts, and no telemetry. The app runs on CPU by... -
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MediaCoder
Universal media transcoding software
MediaCoder is a universal media transcoding software actively developed and maintained since 2005. It puts together most cutting-edge audio/video technologies into an out-of-box transcoding solution with a rich set of adjustable parameters which let you take full control of your transcoding. New features and latest codecs are added or updated constantly. MediaCoder might not be the easiest tool out there, but what matters here is quality and performance. It will be your swiss army knife for... -
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QtAV
A multimedia framework based on Qt and FFmpeg
QtAV is a cross-platform and high performance multimedia playback framework based on Qt and FFmpeg. Features: timeline preview, gpu decoding etc -
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Stable Diffusion in Docker
Run the Stable Diffusion releases in a Docker container
Run the Stable Diffusion releases in a Docker container with txt2img, img2img, depth2img, pix2pix, upscale4x, and inpaint. Run the Stable Diffusion releases on Huggingface in a GPU-accelerated Docker container. By default, the pipeline uses the full model and weights which requires a CUDA capable GPU with 8GB+ of VRAM. It should take a few seconds to create one image. On less powerful GPUs you may need to modify some of the options; see the Examples section for more details. If you lack a suitable GPU you can set the options --device cpu and --onnx instead. ... -
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NAM-Runner
Batch file to install and run NAM (neural-amp-modeler) easily.
...Custom one-time installation of everything you need to train neural network models of guitar amps and more for the NAM VST plugin, no Conda required. Runs as a launcher afterwards. Portable installation. New pyTorch inclues CUDA runtime for fast Nvidia GPU support. No command line, python or conda knowledge needed! Just double click. -
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Real-ESRGAN Video Enhance
Real-ESRGAN video upscaler with resumability
...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. -
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CUDA Pathtracer
GPU Raytracer from scratch in C++/CUDA
GPU-Raytracer is a high-performance, real-time ray tracing engine implemented using OpenGL compute shaders. It demonstrates the power of modern GPU architectures to handle complex lighting calculations, reflections, shadows, and global illumination in real-time. This project is educational and experimental, providing insight into GPU parallelism and real-time rendering techniques. Its clean and modular C++ structure makes it a great reference for students and graphics developers alike. -
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HVVR
Hierarchical visibility for VR and real time rendering of effects
HVVR (Hierarchical Visibility for Virtual Reality) is an optimized software raycaster. It implements a hybrid CPU/GPU raycaster, suited for real-time rendering of effects such as lens distortion. By default, raycaster.vcxproj is configured to build all .cu files to the compute_61,sm_61 target. If your GPU needs a different code-gen target, you can change this in the raycaster project properties. Some installations of the CUDA SDK and Visual Studio integration appear to have broken build dependency tracking. ... -
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viewpix
A Landsat 8 scene viewer
...A 30m resolution gray scale scene is generated by Viewpix and is presented as band 12. Each Landsat 8 scene is roughly 190 X 180 kilometers. Viewpix was originally written as a simple platform to test GPU software on low end computer systems. This is the "lite" version of Viewpix as it has no cuda filters installed. Viewpix is written in C. It is tested on Ubuntu and Centos. It should work with most current Linux distributions. Viewpix runs efficiently on computers with modest CPU's such as low power mini-ITX boards. It is known to work nicely on Intel NUC, AMD A6 SOC , and NVIDIA TX2. ... -
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waifu2x-cpu-torch-vks
waifu2x fork without CUDA
The original is here: https://github.com/nagadomi/waifu2x Differences: - CPU only, no CUDA needed; - double-precision version; - GPU and CPU-trained models (on the fly conversion in memory); - adapted for Lua v5.2 (works with Torch 7 on x32 Ubuntu). 31-12-2016: - removed unneeded data copies, left from CUDA processing; - removed large duplicate files (see "ReadMe" before use). -
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A GUI interface to a tool for generating SSBumps (Self Shadowed Bump Maps). Includes a CUDA GPU rendering extension.
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Accelerated Feature Extraction Tool
A fast GPU accelerated feature extraction software for speech analysis
A fast feature extraction software tool for speech analysis and processing. It incorporates standard MFCC, PLP, and TRAPS features. The tool is a specially designed to process very large audio data sets. It uses GPU acceleration if compatible GPU available (CUDA as weel as OpenCL, NVIDIA, AMD, and Intel GPUs are supported). CPU SSE intrinsic instruction set is used in cases where no compatible GPU present. The output files are stored in HTK format. The software is developed at Department of Cybernetics at University of West Bohemia in Pilsen. -
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HIPAcc
Heterogeneous Image Processing Acceleration (HIPACC) Framework
HIPAcc development has moved to github: https://github.com/hipacc HIPAcc allows to design image processing kernels and algorithms in a domain-specific language (DSL). From this high-level description, low-level target code for GPU accelerators is generated using source-to-source translation. As back ends, the framework supports CUDA, OpenCL, and Renderscript. HIPAcc allows programmers to develop imaging applications while providing high productivity, flexibility and portability as well as competitive performance: the same algorithm description serves as basis for targeting different GPU accelerators and low-level languages. -
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VolTK
A Lightweighted Toolkit For Volume Rendering
A Lightweighted Toolkit For Volume Rendering Project Started by Fei Yang Provide both CPU based and GPU (CUDA) based volume rendering. See http://www.fei-yang.org/?page_id=12 for complete descriptions and package downloads VolTK is a free C++ Toolkit that implements a tri-layer volume rendering framework, which is designed to meet the requirements of both algorithm research and application assemblage. Features * Tri-layer volume rendering * CPU implementation: multi-threading supported * GPU implementation: CUDA based implementation * CPU-GPU correspondance * Both low level interfaces and high level interfaces are provided * Replace the components easily with your own design * Various rendering modes: MIP/Isosurface/Full volume rendering * Endoscopy interaction mode implemented * Pre-integration supported * Cross platform (windows and RHEL tested) -
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Fractron 9000 is a GPU accelerated fractal flame renderer for Windows. Requires a video card that supports OpenGL 2.0 (OpenCL or CUDA recommended). Also Requires Microsoft .NET framework version 2.0.
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Renderer for flam3 cosmic recursive fractal flames implemented on GPU. Requires a CUDA-capable graphics card.
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A fast JPEG2000 encoder running on CUDA-capable GPUs. Supports lossless and lossy encoding. Both commandline tool with GUI and CUDA/C++ library. Visit >> http://cuj2k.sourceforge.net << for further information.
