Search Results for "generative" - Page 2
Sort By:
Showing 59 open source projects for "generative"
View related business solutions-
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.
-
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
1
AI4U
Multi-engine plugin to specify agents with reinforcement learning
...Reinforcement learning promises to overcome traditional navigation mesh mechanisms in games and to provide more autonomous characters. AI4U can be integrated into Imitation Learning through Behavioral Cloning or Generative Adversarial Imitation Learning present on stable-baslines. Train using multiple concurrent Unity/Godot environment instances. Unity/Godot environment partial control from Python. Wrap Unity/Godot learning environments as a gym. -
2
Pixelization
Stable-diffusion-webui-pixelization
...The extension uses pre-trained models and optionally can co-operate with the Web UI’s other features (image-to-image, prompt-based generation) so you can combine pixelization with generative workflows. For digital art, game assets, or retro aesthetic workflows, this offers a fast path from photo or high-res asset to stylized tiles or sprites. Its integration means you don’t need a separate tool or workflow; everything happens via the familiar UI of SD WebUI. -
3
Quil
Main repo, Quil source code
Quil blends Clojure's expressive functional programming with Processing’s creative graphics and animation capabilities, offering a DSL for drawing and interactive visuals in a dynamic, artistic way. In one hand Quil holds Processing, a carefully crafted API for making drawing and animation extremely easy to get your biscuit-loving chops around. In the other she clutches Clojure, an interlocking suite of exquisite language abstractions forged by an army of hammocks and delicately wrapped in... -
4
GPT-Code UI
An open source implementation of OpenAI's ChatGPT Code interpreter
An open source implementation of OpenAI's ChatGPT Code interpreter. Simply ask the OpenAI model to do something and it will generate & execute the code for you. You can put a .env in the working directory to load the OPENAI_API_KEY environment variable. For Azure OpenAI Services, there are also other configurable variables like deployment name. See .env.azure-example for more information. Note that model selection on the UI is currently not supported for Azure OpenAI Services. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
-
5
Coframe
Coframe brings your UX to life with AI-powered optimization
Bring your UX to life with AI-powered optimization and personalization. Coframe brings the content of your app or website to life through AI-powered optimization, personalization, and overall self-improvement. It takes minutes to integrate, and the ROI is clear to measure. Your website or app gains self-enhancing abilities with Coframe, learning from real-world performance. It's A/B testing, but with a serious upgrade. Coframe uses the latest in AI to generate copy that is tailored to your... -
6
iJEPA
Official codebase for I-JEPA
...A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer strongly with linear probes and minimal fine-tuning. The design scales naturally with Vision Transformer backbones and flexible masking strategies, and it trains stably at large batch sizes. i-JEPA’s predictions are made in embedding space, which is computationally efficient and better aligned with downstream discrimination tasks. ... -
7
DeepMind Research
Implementations and code to accompany DeepMind publications
This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics, MeshGraphNets for physics, RL Unplugged, Nowcasting for weather, and more. Each project folder typically includes its own README, scripts, and notebooks so you can run experiments or explore models in isolation, and many link to associated datasets or external environments like DeepMind Lab and StarCraft II. ... -
8
canvas-sketch
Framework for making generative artwork in JavaScript and the browser
canvas-sketch is a JavaScript framework designed to streamline the creation of generative art, creative coding experiments, and interactive graphics. It provides a lightweight scaffolding environment that helps developers and artists quickly prototype visual projects using HTML5 Canvas or WebGL. The tool focuses on reducing boilerplate so creators can concentrate on visual logic and animation rather than setup. It integrates smoothly with modern JavaScript workflows and supports modular development patterns. ... -
9
Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
Real-ESRGAN is a highly popular open-source project that provides practical algorithms for general image and video restoration using deep learning-based super-resolution techniques. It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. ... -
Earn up to 16% annual interest with Nexo.Put idle assets to work with competitive interest rates, borrow without selling, and trade with precision. All in one platform. Geographic restrictions, eligibility, and terms apply.
-
10
Blend_My_NFTs
Easily generate thousands of 3D models, images, and animation NFTs
Blend_My_NFTs is an open-source, free-to-use Blender add-on that enables you to easily generate thousands of 3D Models, Animations, and Images. This add-on's primary purpose is to aid in the creation of large generative 3D NFT collections. It is the first and easiest 3D NFT generator. Blend_My_NFTs was initially developed to create Cozy Place, an NFT collection by This Cozy Studio Inc. Blend_My_NFTs works with Blender 3.2.2 on Windows 10 or macOS Big Sur 11.6. Linux is supported, however we haven't had the chance to test and guarantee this functionality. ... -
11
MarkovJunior
Probabilistic language based on pattern matching
...The system is highly flexible, supporting both deterministic and probabilistic rule application to generate diverse results. It is particularly popular in game development and generative art, where procedural content can enhance variability and creativity. The project provides tools for defining rules, visualizing outputs, and experimenting with different configurations. Its design emphasizes simplicity while still enabling complex emergent behavior. Overall, MarkovJunior is a powerful framework for procedural generation using rule-based approaches. -
12
pyTorch Tutorials
Build your neural network easy and fast
...The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks, recurrent networks, autoencoders, and generative adversarial networks, which gives learners practical exposure to real machine learning tasks. Each example explains PyTorch’s dynamic computation graph, optimization techniques, and core abstractions in a way that is accessible and reproducible. Contributors and authors integrate visual and coded examples so readers can see both the theory and the implementation side-by-side. -
13
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. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to... -
14
glfx.js
An image effects library for JavaScript using WebGL
...A demo page showcases a variety of filters, from basic brightness/contrast adjustments to more advanced distortions and color manipulations. glfx.js remains a handy reference and solution for anyone needing client-side photo editing, visual effects, or generative graphics powered by WebGL. -
15
TorchGAN
Research Framework for easy and efficient training of GANs
The torchgan package consists of various generative adversarial networks and utilities that have been found useful in training them. This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. -
16
DockStream
A Docking Wrapper to Enhance De Novo Molecular Design
...DockStream can also parallelize docking across CPU cores, increasing throughput. DockStream is integrated with the de novo design platform, REINVENT, allowing one to incorporate docking into the generative process, thus providing the agent with 3D structural information. -
17
ML for Trading
Code for machine learning for algorithmic trading, 2nd edition
...The design and evaluation of long-short strategies based on a broad range of ML algorithms, how to extract tradeable signals from financial text data like SEC filings, earnings call transcripts or financial news. Using deep learning models like CNN and RNN with financial and alternative data, and how to generate synthetic data with Generative Adversarial Networks, as well as training a trading agent using deep reinforcement learning. -
18
HyperGAN
Composable GAN framework with api and user interface
A composable GAN built for developers, researchers, and artists. HyperGAN builds generative adversarial networks in PyTorch and makes them easy to train and share. HyperGAN is currently in pre-release and open beta. Everyone will have different goals when using hypergan. HyperGAN is currently beta. We are still searching for a default cross-data-set configuration. Each of the examples supports search. Automated search can help find good configurations. -
19
pytorch-examples
Simple examples to introduce PyTorch
...It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. By emphasizing readable code, the repository helps users understand how PyTorch’s imperative programming style enables flexible model development. It also serves as a quick reference for common patterns and techniques used in deep learning workflows. ... -
20
TGAN
Generative adversarial training for generating synthetic tabular data
We are happy to announce that our new model for synthetic data called CTGAN is open-sourced. The new model is simpler and gives better performance on many datasets. TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns. TGAN has been developed and runs on Python 3.5, 3.6 and 3.7. Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid... -
21
Compare GAN
Compare GAN code
compare_gan is a research codebase that standardizes how Generative Adversarial Networks are trained and evaluated so results are comparable across papers and datasets. It offers reference implementations for popular GAN architectures and losses, plus a consistent training harness to remove confounding differences in optimization or preprocessing. The library’s evaluation suite includes widely used metrics and diagnostics that quantify sample quality, diversity, and mode coverage. ... -
22
Edward
A probabilistic programming language in TensorFlow
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It... -
23
Keras resources
Directory of tutorials and open-source code repositories
...It aggregates a wide range of resources, including beginner guides, advanced tutorials, code examples, and third-party tools, all organized into a single reference hub. The repository covers diverse topics such as image classification, natural language processing, reinforcement learning, and generative models, providing both theoretical and practical insights. It also includes links to external projects built with Keras, demonstrating real-world applications of deep learning techniques. The structure is designed for easy navigation, allowing users to quickly find relevant materials based on their skill level or area of interest. ... -
24
EaselJS
Work with the HTML5 Canvas element easily
The Easel Javascript library provides a full, hierarchical display list, a core interaction model, and helper classes to make working with the HTML5 Canvas element much easier. A JavaScript library that makes working with the HTML5 Canvas element easy. Useful for creating games, generative art, and other highly graphical experiences. EaselJS provides straight forward solutions for working with rich graphics and interactivity with HTML5 Canvas. It provides an API that is familiar to Adobe Animate developers, but embraces JavaScript sensibilities. It consists of a full, hierarchical display list, a core interaction model, and helper classes to make working with Canvas much easier. ... -
25
Grenade
Deep Learning in Haskell
Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only...
