Open Source Windows Generative AI - Page 4
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DeepMozart
Audio generation using diffusion models
Audio generation using diffusion models in PyTorch. The code is based on the audio-diffusion-pytorch repository. -
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GPT-NeoX
Implementation of model parallel autoregressive transformers on GPUs
This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training. For those looking for a TPU-centric codebase, we recommend Mesh Transformer JAX. If you are not looking to train models with billions of parameters from scratch, this is likely the wrong library to use. For generic inference needs, we recommend you use the Hugging Face transformers library instead which supports GPT-NeoX models. -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). The general recommendation is to use suitable augs for your data and as many as possible, then after some time of training disable the most destructive (for image) augs. You can turn on automatic mixed precision with one flag --amp. You should expect it to be 33% faster and save up to 40% memory. Aim is an open-source experiment tracker that logs your training runs, and enables a beautiful UI to compare them. -
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LlamaIndex
Central interface to connect your LLM's with external data
LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. LlamaIndex is a simple, flexible interface between your external data and LLMs. It provides the following tools in an easy-to-use fashion. Provides indices over your unstructured and structured data for use with LLM's. These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when the context is too big. Offers you a comprehensive toolset, trading off cost and performance. -
Ditto Edge Server is a lightweight standalone server for resource-constrained edge environments, based on the core Ditto Edge SDK.Ditto's Edge SDK is the only thing your edge devices need to ensure your application is operational in any environment, regardless of network conditions.
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Megatron
Ongoing research training transformer models at scale
Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel (tensor, sequence, and pipeline), and multi-node pre-training of transformer based models such as GPT, BERT, and T5 using mixed precision. Megatron is also used in NeMo Megatron, a framework to help enterprises overcome the challenges of building and training sophisticated natural language processing models with billions and trillions of parameters. Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. -
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NWT - Pytorch (wip)
Implementation of NWT, audio-to-video generation, in Pytorch
Implementation of NWT, audio-to-video generation, in Pytorch. The paper proposes a new discrete latent representation named Memcodes, which can be succinctly described as a type of multi-head hard-attention to learned memory (codebook) key/values. They claim the need for less codes and smaller codebook dimensions in order to achieve better reconstructions. -
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Recurrent Interface Network (RIN)
Implementation of Recurrent Interface Network (RIN)
Implementation of Recurrent Interface Network (RIN), for highly efficient generation of images and video without cascading networks, in Pytorch. The author unawaredly reinvented the induced set-attention block from the set transformers paper. They also combine this with the self-conditioning technique from the Bit Diffusion paper, specifically for the latents. The last ingredient seems to be a new noise function based around the sigmoid, which the author claims is better than cosine scheduler for larger images. The big surprise is that the generations can reach this level of fidelity. Will need to verify this on my own machine. Additionally, we will try adding an extra linear attention on the main branch as well as self-conditioning in the pixel space. The insight of being able to self-condition on any hidden state of the network as well as the newly proposed sigmoid noise schedule are the two main findings. -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similar, semantic search, or paraphrase mining. The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. Further, it is easy to fine-tune your own models. Our models are evaluated extensively and achieve state-of-the-art performance on various tasks. Further, the code is tuned to provide the highest possible speed. -
Stigg | SaaS Monetization and Entitlements APIA monetization platform is a standalone middleware that sits between your application and your business applications, as part of the modern enterprise billing stack. Stigg unifies all the APIs and abstractions billing and platform engineers had to build and maintain in-house otherwise. Acting as your centralized source of truth, with a highly scalable and flexible entitlements management, rolling out any pricing and packaging change is now a self-service, risk-free, exercise.
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StoryTeller
Multimodal AI Story Teller, built with Stable Diffusion, GPT, etc.
A multimodal AI story teller, built with Stable Diffusion, GPT, and neural text-to-speech (TTS). Given a prompt as an opening line of a story, GPT writes the rest of the plot; Stable Diffusion draws an image for each sentence; a TTS model narrates each line, resulting in a fully animated video of a short story, replete with audio and visuals. To develop locally, install dev dependencies and install pre-commit hooks. This will automatically trigger linting and code quality checks before each commit. The final video will be saved as /out/out.mp4, alongside other intermediate images, audio files, and subtitles. For more advanced use cases, you can also directly interface with Story Teller in Python code. -
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TextBox
A text generation library with pre-trained language models github.com
TextBox 2.0 is an up-to-date text generation library based on Python and PyTorch focusing on building a unified and standardized pipeline for applying pre-trained language models to text generation. From a task perspective, we consider 13 common text generation tasks such as translation, story generation, and style transfer, and their corresponding 83 widely-used datasets. From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules). From a training perspective, we support 4 pre-training objectives and 4 efficient and robust training strategies, such as distributed data parallel and efficient generation. Compared with the previous version of TextBox, this extension mainly focuses on building a unified, flexible, and standardized framework for better supporting PLM-based text generation models. -
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canvas-constructor
An ES6 utility for canvas with built-in functions and chained methods
An ES6 utility for canvas with built-in functions and chained methods. Alternatively, you can import canvas-constructor/browser. That will create a canvas with size of 300 pixels width, 300 pixels height. Set the color to #AEFD54. Draw a rectangle with the previous color, covering all the pixels from (5, 5) to (290 + 5, 290 + 5) Set the color to #FFAE23. Set the font size to 28 pixels with font Impact. Write the text 'Hello World!' in the position (130, 150) Return a buffer. -
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pdf-extractor
Node.js module for rendering pdf pages to images, svgs and HTML files
Pdf-extractor is a wrapper around pdf.js to generate images, svgs, html files, text files and json files from a pdf on node.js. A DOM Canvas is used to render and export the graphical layer of the pdf. Canvas exports *.png as a default but can be extended to export to other file types like .jpg. Pdf objects are converted to svg using the SVGGraphics parser of pdf.js. Pdf text is converted to HTML. This can be used as a (transparent) layer over the image to enable text selection. Pdf text is extracted to a text file for different usages (e.g. indexing the text). This library is in it's most basic form a node.js wrapper for pdf.js. It has default renderers to generate a default output, but is easily extended to incorporate custom logic or to generate different output. It uses a node.js DOM and the node domstub from pdf.js do make pdf parsing available on node.js without a browser. -
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revChatGPT
This app allows you to chat with ChatGPT using reverse-engineered API
This app allows you to chat with ChatGPT using a reverse-engineered API library called revChatGPT. Replies from the Chatbot are streamed back to the user in real-time, which gives the user an experience similar to how ChatGPT streams back its answers. To get started with the app, you'll need to create an account on OpenAI's ChatGPT and save your credentials. You can choose from three authentication methods: Email/Password, Session token, or Access token. Once you have your credentials, you can select your authentication method in the sidebar and provide the required information. If you choose Email/Password, you'll need to provide your email and password. If you choose Session token, you'll need to provide your session token. If you choose Access token, you'll need to provide your access token. revChatGPT is a reverse-engineered ChatGPT API that is not affiliated with OpenAI. It is intended for educational and research purposes only. -
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ruDALL-E
Generate images from texts. In Russian
We present a family of generative models from SberDevices and Sber AI! Models allow you to create images that did not exist before. All you need is a text description in Russian or another language. Try to create unique images together with generative artists using your own formulations. Ask generative artists to depict something special for you as well. The Kandinsky 2.0 model uses the reverse diffusion method and creates colorful images on various topics in a matter of seconds by text query in Russian and other languages. You can even combine different languages within a single query. This neural network has been developed and trained by Sber AI researchers in close collaboration with scientists from Artificial Intelligence Research Institute using joined datasets by Sber AI and SberDevices. Russian text-to-image model that generates images from text. The architecture is the same as ruDALL-E XL. Even more parameters in the new version. -
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AppFlowy
Bring projects, wikis, and teams together with AI.
AppFlowy is an AI collaborative workspace where you can achieve more without losing control of your data. It is the best open source alternative to Notion, offering a 100% offline mode and self-hosting with a cloud service of your choice. Build a centralized workspace for your wiki, projects, and notes with AppFlowy. It allows you to organize and visualize your data in tables, Kanban boards, calendars, and more. You can filter and sort your data in any way you want. AppFlowy comes with a beautiful rich-text editor that goes beyond just text and bullet points, offering 20+ content types, easy-to-use customized themes, keyboard shortcuts, and color options. It supports real-time team collaboration, enabling you to work with your friends and teammates on the same document in real time, similar to Google Docs. AppFlowy is powered by AppFlowy AI, which is accessible, collaborative, and contextual. Supercharge any type of work in a collaborative team workspace. -
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A Netflix film cover generator Nuxt.js
A tool for generating Netflix show image
We love Netflix, but we love memes even more. We thought that helping Netflix on their UI/UX testing with a tool that can create show images easily with an export function to png. A tool for generating Netflix shows an image. You can visit the demo website hosted on Netlify. This is an open-source tool and it is available on Github. On this tool you have a full editable canvas where you can edit content, text position, text dimension, gradient position and change the background image. In order to change the element position you can just click and drag anywhere. Meanwhile, if yuo want to change the content inside an element you need to double-click on it. By double clicking on an element it will show a textarea where you can edit and confirm the changes by clicking elsewhere or by clicking Enter. In order to change the background image you can drag-n-drop any image onto the canvas and it will change the background image. -
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AI Chatbots based on GPT Architecture
Training & Implementation of chatbots leveraging GPT-like architecture
Training & Implementation of chatbots leveraging GPT-like architecture with the aitextgen package to enable dynamic conversations. It sure seems like there are a lot of text-generation chatbots out there, but it's hard to find a python package or model that is easy to tune around a simple text file of message data. This repo is a simple attempt to help solve that problem. ai-msgbot covers the practical use case of building a chatbot that sounds like you (or some dataset/persona you choose) by training a text-generation model to generate conversation in a consistent structure. This structure is then leveraged to deploy a chatbot that is a "free-form" model that consistently replies like a human. Some of the trained models can be interacted with through the HuggingFace spaces and model inference APIs on the ETHZ Analytics Organization page on huggingface.co. -
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Accelerated Text
Accelerated Text is a no-code natural language generation platform
A picture is worth a thousand words. Or is it? Tables, charts, pictures are all useful in understanding our data but often we need a description – a story to tell us what are we looking at. Accelerated Text is a natural language generation tool which allows you to define data descriptions and then generates multiple versions of those descriptions varying in wording and structure. Accelerated Text is a no-code natural language generation platform. It will help you construct document plans which define how your data is converted to textual descriptions. With Accelerated Text you can use such data to generate text for your business reports, your e-commerce platform or your customer support system. Data descriptions require precision. Accelerated Text follows the principle of this strict adherence to data-bound text generation. Via its user interface, it provides instruments to define how the data should be translated into a descriptive text. -
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Ad Generator
Professional text randomizer and ad generator by Airat Khalitov
Professional text randomizer and ad generator by Airat Khalitov / Professional text randomizer and ad generator. Author: Airat Halitov. Visit 'Plugins, Add New', click 'Upload Plugin', upload the file 'ad-generator.zip', and activate Ad Generator from your Plugins page. Add [ad_generator] shortcode to WordPress Page. Create a new WordPress Page, add [ad_generator] shortcode and save. Go to the page and use the ad generator. This is a program for industrial creation of pseudo-unique content. Used, for example, when registering a site in multiple directories. So that in each directory the site is described by text that is unique from the point of view of search engines. Unlike similar tools (synonymizers, dorgens), it allows you to maximize the readability of the resulting texts. -
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Aida Lib
Aida is a language agnostic library for text generation
Aida is a language-agnostic library for text generation. When using Aida, first you compose a tree of operations on your text that includes conditions via branches and other control flow. Later, you fill the tree with data and render the text. A building block is a variable class: Var. Use it to represent a value that you want to control later. A variable can hold numbers (e.g. float, int) or strings. You can create branches and complex logic with Branch. The context, represented by the class Ctx, is useful to create rules that depends on what has been written before. Each object or literal that is passed to Aida is remembered by the context. Creating a reference expression is a common use-case, so we have a helper function called create_ref. You can compose operations on your text with some handy operators. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. Albumentations supports different computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation. Albumentations works well with data from different domains: photos, medical images, satellite imagery, manufacturing and industrial applications, Generative Adversarial Networks. Albumentations can work with various deep learning frameworks such as PyTorch and Keras. -
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Amiga Memories
A walk along memory lane
Amiga Memories is a project (started & released in 2013) that aims to make video programmes that can be published on the internet. The images and sound produced by Amiga Memories are 100% automatically generated. The generator itself is implemented in Squirrel, the 3D rendering is done on GameStart 3D. An Amiga Memories video is mostly based on a narrative. The purpose of the script is to define the spoken and written content. The spoken text will be read by a voice synthesizer (Text To Speech or TTS), the written text is simply drawn on the image as subtitles. Here, in addition to the spoken & written narration, the script controls the camera movements as well as the LED activity of the computer. Amiga Memories' video images are computed by the GameStart 3D engine (pre-HARFANG 3D). Although the 3D assets are designed to be played back in real-time with a variable framerate, the engine is capable of breaking down the video sequence into the 30th or 60th of a second, as TGA files. -
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AnimeGAN
A simple PyTorch Implementation of Generative Adversarial Networks
A simple PyTorch Implementation of Generative Adversarial Networks, focusing on anime face drawing. The images are generated from a DCGAN model trained on 143,000 anime character faces for 100 epochs. Manipulating latent codes enables the transition from images in the first row to the last row. The images are not clean, some outliers can be observed, which degrades the quality of the generated images. Anime-style images of 126 tags are collected from danbooru.donmai.us using the crawler tool gallery-dl. The images are then processed by an anime face detector python-anime face. The resulting dataset contains ~143,000 anime faces. Note that some of the tags may no longer be meaningful after cropping, i.e. the cropped face images under the 'uniform' tag may not contain visible parts of uniforms. -
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AudioGenerator
Generates a sound given: volume, frequency, duration
Generates a sound given: volume, frequency, duration! Download build.zip, unpack zip, and run the executable.