Search Results for "gpu image" - Page 5
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Showing 175 open source projects for "gpu image"
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Roop
One-click face swap
Take a video and replace the face with a face of your choice. You only need one image of the desired face. No dataset, and no training. -
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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. ... -
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Exadel CompreFace
Leading free and open-source face recognition system
Exadel CompreFace is a free and open-source face recognition GitHub project. Essentially, it is a docker-based application that can be used as a standalone server or deployed in the cloud. You don’t need prior machine learning skills to set up and use CompreFace. The system provides REST API for face recognition, face verification, face detection, face mask detection, landmark detection, age, and gender recognition. The solution also features a role management system that allows you to... -
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MetalPetal
A GPU accelerated image and video processing framework built on Metal
MetalPetal is an image processing framework based on Metal designed to provide real-time processing for still images and video with easy-to-use programming interfaces. This chapter covers the key concepts of MetalPetal, and will help you to get a better understanding of its design, implementation, performance implications, and best practices. A MTIImage object is a representation of an image to be processed or produced. It does directly represent image bitmap data instead it has all the... -
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Knet
Koç University deep learning framework
...Here are step-by-step instructions for launching a GPU instance with a Knet image (the screens may have changed slightly since this writing). -
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GLSL-PathTracer
A toy physically based GPU path tracer (C++/OpenGL/GLSL)
A physically based path tracer that runs in a GLSL fragment shader. -
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Point-E
Point cloud diffusion for 3D model synthesis
point-e is the official repository for Point-E, a generative model developed by OpenAI that produces 3D point clouds from textual (or image) prompts. Its principal advantage is speed: it can generate 3D assets in just 1–2 minutes on a single GPU, which is significantly faster than many competing text-to-3D models. The model works via a two-stage diffusion approach: first, it uses a text → image diffusion network to produce a synthetic 2D view consistent with the prompt; then a second diffusion model converts that image into a 3D point cloud. ... -
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Stable Diffusion
A latent text-to-image diffusion model
Stable Diffusion is a widely used open-source latent text-to-image diffusion model developed by the CompVis group for generating high-quality images from natural language prompts. The model operates by conditioning a diffusion process on text embeddings produced by a CLIP text encoder, enabling detailed and controllable image synthesis. It was trained on large-scale image datasets and later fine-tuned to produce 512×512 images with strong visual fidelity. Because the system runs efficiently... -
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G-Diffuser Bot
Discord bot and Interface for Stable Diffusion
The first release of the all-in-one installer version of G-Diffuser is here. This release no longer requires the installation of WSL or Docker and has a systray icon to keep track of and launch G-Diffuser components. The infinite zoom scripts have been updated with some improvements, notably a new compositer script that is hundreds of times faster than before. The first release of the all-in-one installer is here. It notably features much easier "one-click" installation and updating, as well... -
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GFPGAN
GFPGAN aims at developing Practical Algorithms
...Colab Demo for GFPGAN; (Another Colab Demo for the original paper model) Online demo: Huggingface (return only the cropped face) Online demo: Replicate.ai (may need to sign in, return the whole image). Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. ... -
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BCI
BCI: Breast Cancer Immunohistochemical Image Generation
Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix. We have released the trained model on BCI and LLVIP datasets. We host a competition for breast cancer immunohistochemistry image generation on Grand Challenge. Project pix2pix provides a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene, these can be pairs {HE, IHC}. Then we can learn to translate A(HE images)... -
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Diffusers-Interpret
Model explainability for Diffusers
...Gradient checkpointing also reduces GPU usage, but makes computations a bit slower. -
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Darknet YOLO
Real-Time Object Detection for Windows and Linux
This is YOLO-v3 and v2 for Windows and Linux. YOLO (You only look once) is a state-of-the-art, real-time object detection system of Darknet, an open source neural network framework in C. YOLO is extremely fast and accurate. It uses a single neural network to divide a full image into regions, and then predicts bounding boxes and probabilities for each region. This project is a fork of the original Darknet project. -
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Darknet
Convolutional Neural Networks
...Darknet is lightweight, fast, and easy to compile, making it suitable for research and production use. The repository provides pre-trained models, configuration files, and tools for training custom object detection models. With GPU acceleration via CUDA and OpenCV integration, it achieves high performance in image recognition tasks. Its simplicity, combined with powerful capabilities, has made Darknet one of the most influential projects in the computer vision community. -
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min(DALL·E)
min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
...Every image token is sampled from the top_k most probable tokens. The largest logit is subtracted from the logits to avoid infs. The logits are then divided by the temperature. If is_seamless is true, the image grid will be tiled in token space not pixel space. -
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YOLOV4 Pytorch
This is a source code for YoloV4-pytorch that can be used to train you
...It supports VOC-style datasets and includes scripts for prediction, mAP evaluation, FPS testing, video prediction, batch prediction, and heatmap generation. The project added multi-GPU training, seed settings for reproducible results, adaptive learning rate behavior based on batch size, and both step and cosine learning rate schedules. It also supports Adam and SGD optimizer choices, image cropping, adjustable parameters, and extensive code comments. It is a useful educational and applied repository for users who want to understand or customize YOLOv4 in PyTorch. -
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YOLOV3 Pytorch
This is a source code for yolo3-pytorch
...The repository provides a complete workflow for users who want to train their own object detector with VOC-style data or use pretrained weights. It includes utilities for annotation conversion, anchor generation, image prediction, video prediction, batch prediction, FPS measurement, heatmap output, and mAP evaluation. The project added multi-GPU training, target count statistics, learning rate scheduling with step and cosine options, and optimizer selection between Adam and SGD. It also includes adaptive learning rate adjustment based on batch size, image cropping, many configurable parameters, and expanded comments for easier study. ... -
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Ion
Portable suite of libraries and tools for building client applications
Ion is a modular C++ toolkit for building high-performance 2D/3D graphics applications with a strong emphasis on portability, correctness, and developer ergonomics. Rather than a monolithic engine, it offers focused libraries—math, image, GPU resource management, shader utilities, remote inspection, and platform abstractions—that you can adopt à la carte. The rendering layer wraps modern OpenGL/OpenGL ES concepts with a carefully layered API that tracks object lifetimes, deduplicates resources, and enables safe multithreaded recording of draw calls. Asset utilities handle image formats, texture compression, and color management so pipelines can stay consistent across desktop and mobile GPUs. ... -
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Tensorflow Transformers
State of the art faster Transformer with Tensorflow 2.0
...Supports GPU, TPU, multi-GPU trainer with wandb, multiple callbacks, auto tensorboard. -
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glfx.js
An image effects library for JavaScript using WebGL
glfx.js is a JavaScript image-effects library that uses WebGL to apply real-time filters and transformations directly in the browser. It exposes a simple API where images are uploaded into GPU textures, processed with shader-based filters, and rendered to a WebGL canvas. Because the work is done on the GPU, many effects that would be too slow in pure JavaScript (like complex blurs, lens effects, or tilt-shift) can run interactively, even on large images. -
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Big Sleep
A simple command line tool for text to image generation
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. -
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
VQGAN-CLIP has been in vogue for generating art using deep learning. Searching the r/deepdream subreddit for VQGAN-CLIP yields quite a number of results. Basically, VQGAN can generate pretty high-fidelity images, while CLIP can produce relevant captions for images. Combined, VQGAN-CLIP can take prompts from human input, and iterate to generate images that fit the prompts. Thanks to the generosity of creators sharing notebooks on Google Colab, the VQGAN-CLIP technique has seen widespread... -
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CLIP Guided Diffusion
A CLI tool/python module for generating images from text
A CLI tool/python module for generating images from text using guided diffusion and CLIP from OpenAI. Text to image generation (multiple prompts with weights). Non-square Generations (experimental) Generate portrait or landscape images by specifying a number to offset the width and/or height. Uses fewer timesteps over the same diffusion schedule. Sacrifices accuracy/alignment for quicker runtime. options: - 25, 50, 150, 250, 500, 1000, ddim25,ddim50,ddim150, ddim250,ddim500,ddim1000... -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll... -
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gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
...Additionally, this package allows easier generation of text, generating to a file for easy curation, allowing for prefixes to force the text to start with a given phrase. For finetuning, it is strongly recommended to use a GPU, although you can generate using a CPU (albeit much more slowly). If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended. (as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. ...
