Open Source Generative AI - Page 3
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Phenaki - Pytorch
Implementation of Phenaki Video, which uses Mask GIT
Implementation of Phenaki Video, which uses Mask GIT to produce text-guided videos of up to 2 minutes in length, in Pytorch. It will also combine another technique involving a token critic for potentially even better generations. A new paper suggests that instead of relying on the predicted probabilities of each token as a measure of confidence, one can train an extra critic to decide what to iteratively mask during sampling. This repository will also endeavor to allow the researcher to train on text-to-image and then text-to-video. Similarly, for unconditional training, the researcher should be able to first train on images and then fine tune on video. -
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Quote2Image
A Python library for turning text quotes into graphical images
A Python library for turning text quotes into graphical images. Generate an image using RGB background and foreground. The package comes with a built-in GenerateColors function that generates a fg and bg color with the correct amount of luminosity and returns them in tuples. Generate an image using a custom background image. The package comes with a builtin GenerateColors function that generates a fg and bg color with the correct amount of luminosity and returns them in tuples. We can generate an image using a custom background image using the ImgObject that gives us alot of flexibility on how we want our background Image to be. You are allowed to use, modify, and distribute the module. You are allowed to distribute modified versions of the module, as long as you follow the terms of the license. -
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Stable Diffusion v 2.1 web UI
Lightweight Stable Diffusion v 2.1 web UI: txt2img, img2img, depth2img
Lightweight Stable Diffusion v 2.1 web UI: txt2img, img2img, depth2img, in paint and upscale4x. Gradio app for Stable Diffusion 2 by Stability AI. It uses Hugging Face Diffusers implementation. Currently supported pipelines are text-to-image, image-to-image, inpainting, upscaling and depth-to-image. -
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TextGen
textgen, Text Generation models
Implementation of Text Generation models. textgen implements a variety of text generation models, including UDA, GPT2, Seq2Seq, BART, T5, SongNet and other models, out of the box. UDA, non-core word replacement. EDA, simple data augmentation technique: similar words, synonym replacement, random word insertion, deletion, replacement. This project refers to Google's UDA (non-core word replacement) algorithm and EDA algorithm, based on TF-IDF to replace some unimportant words in sentences with synonyms, random word insertion, deletion, replacement, etc. method, generating new text and implementing text augmentation This project realizes the back translation function based on Baidu translation API, first translate Chinese sentences into English, and then translate English into new Chinese. This project implements the training and prediction of Seq2Seq, ConvSeq2Seq, and BART models based on PyTorch, which can be used for text generation tasks such as text translation. -
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min(DALL·E)
min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
This is a fast, minimal port of Boris Dayma's DALL·E Mini (with mega weights). It has been stripped down for inference and converted to PyTorch. The only third-party dependencies are numpy, requests, pillow and torch. The required models will be downloaded to models_root if they are not already there. Set the dtype to torch.float16 to save GPU memory. If you have an Ampere architecture GPU you can use torch.bfloat16. Set the device to either cuda or "cpu". Once everything has finished initializing, call generate_image with some text as many times as you want. Use a positive seed for reproducible results. Higher values for supercondition_factor result in better agreement with the text but a narrower variety of generated images. 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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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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pytorch-cpp
C++ Implementation of PyTorch Tutorials for Everyone
C++ Implementation of PyTorch Tutorials for Everyone. This repository provides tutorial code in C++ for deep learning researchers to learn PyTorch (i.e. Section 1 to 3) Interactive Tutorials are currently running on LibTorch Nightly Version. Libtorch only supports 64bit Windows and an x64 generator needs to be specified. Create all required script module files for pre-learned models/weights during the build. Requires installed python3 with PyTorch and torch-vision. You can choose to only build tutorials in one of the categories basics, intermediate, advanced or popular. You can build and run the tutorials (on CPU) in a Docker container using the provided Dockerfile and docker-compose.yml files. -
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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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texturize
Generate photo-realistic textures based on source images
Generate photo-realistic textures based on source images. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture. A command-line tool and Python library to automatically generate new textures similar to a source image or photograph. It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. This software is powered by deep learning technology, using a combination of convolution networks and example-based optimization to synthesize images. We're building texturize as the highest-quality open source library available! The examples are available as notebooks, and you can run them directly in-browser thanks to Jupyter and Google Colab. -
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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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uweb browser: unlimited power
minimal suckless android web browser with unlimited power
- Powerful: html5 enhancement; any urls to host a website; javascript and shell scripting for general processing; and more with Termux. - Customizable: user-defined menus, (new) buttons and gestures for user agents, bookmarklets, url services, shell commands, internal functionality links and text processing etc. - Convenient: Any book (pdf/djvu)/dictionary (mdict)/txt/command line/app/webapp (web extensions) can be search engine. - Tiny: less than 200k - Fast: run fast, even with thousands of user provided css/scripts - Efficient: less touches, one click to reach any number of search engines without repeated input; automate online services. - URL bar command line support ("!" and .js files as commands). - user-defined site-specific JS/CSS/HTML/preprocessing. - Online play/preview/preprocess for downloadable resources. - Multiple type profiles: switch any data including logins/config orthogonally - web automation, crontab (alarm clock & more) -
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Infinite Sides
Infinite Craft but in Pyside6 and Python with local LLM
Infinite Craft but in Pyside6 and Python with local LLM (llama2 & others) using Ollama that also lets you create your own crafting game based on any topic Customize the game any way you like in the settings. -
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AI Models Downloads
Open Source AI Models Your One-Stop Destination for AI Model Downloads
The download is a sample Voice Pack Trained and Used with Coqui TTS AIModels.org provides a comprehensive directory of AI models for download, ranging from text-to-speech, large language models, to text-to-image models. Our goal is to provide you with easy access to a vast array of pre-trained models, complete with examples and demonstrations. Discover the Power of AI Models AI models have come a long way, empowering various applications that improve our lives on a daily basis. Here are just a few examples of the capabilities available: Text-to-Speech: Convert written text into audible speech, enhancing user experience or assisting those with limited reading abilities. Large Language Models: Generate human-like responses to textual input, providing a transformative basis for chatbots, cohesive summaries, and much more. -
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Conversations
App in java for chatting to a generative A.I. (involving tts and stt)
Java application for chatting to generative AI Llama3. * The user can speak into the microphone (speechToText), edit the recognized text and send it to the AI. * The AI responds and the server returns that response in real time, and the sentences converted to audio (textToSpeech), and the application broadcasts them through the speaker. The application is prepared so that only one user occupies the server's resources, so if the server is busy, in theory it will not let you connect. There is a demo video that shows how it works: https://frojasg1.com:8443/resource_counter/resourceCounter?operation=countAndForward&url=https%3A%2F%2Ffrojasg1.com%2Fdemos%2Faplicaciones%2Fchat%2F20240815.Demo.Chat.mp4%3Forigin%3Dsourceforge&origin=web -
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Langfuse
Open-source observability and analytics for LLM apps
Langfuse is building open-source observability and analytics for LLM apps. Observability: Explore and debug complex logs & traces in a visual UI. Analytics: Improve performance of LLM apps. In particular, get a view on costs, latency and response quality using intuitive dashboards. -
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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 Atelier
Based on the Disco Diffusion, version of the AI art creation software
Based on the Disco Diffusion, we have developed a Chinese & English version of the AI art creation software "AI Atelier". We offer both Text-To-Image models (Disco Diffusion and VQGAN+CLIP) and Text-To-Text (GPT-J-6B and GPT-NEOX-20B) as options. Making available complete source code of licensed works and modifications, which include larger works using a licensed work, under the same license. Copyright and license notices must be preserved. When a modified version is used to provide a service over a network, the complete source code of the modified version must be made available. Create 2D and 3D animations and not only still frames (from Disco Diffusion v5 and VQGAN Animations). Input audio and images for generation instead of just text. Simplify tool setup process on colab, and enable ‘one-click’ sharing of the generated link to other users. Experiment with the possibilities for multi-user access to the same link. -
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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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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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Aphantasia
CLIP + FFT/DWT/RGB = text to image/video
This is a collection of text-to-image tools, evolved from the artwork of the same name. Based on CLIP model and Lucent library, with FFT/DWT/RGB parameterizes (no-GAN generation). Illustrip (text-to-video with motion and depth) is added. DWT (wavelets) parameterization is added. Check also colabs below, with VQGAN and SIREN+FFM generators. Tested on Python 3.7 with PyTorch 1.7.1 or 1.8. Generating massive detailed textures, a la deepdream, fullHD/4K resolutions and above, various CLIP models (including multi-language from SBERT), continuous mode to process phrase lists (e.g. illustrating lyrics), pan/zoom motion with smooth interpolation. Direct RGB pixels optimization (very stable) depth-based 3D look (courtesy of deKxi, based on AdaBins), complex queries: text and/or image as main prompts, separate text prompts for style and to subtract (avoid) topics. Starting/resuming process from saved parameters or from an image. -
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AudioLM - Pytorch
Implementation of AudioLM audio generation model in Pytorch
Implementation of AudioLM, a Language Modeling Approach to Audio Generation out of Google Research, in Pytorch It also extends the work for conditioning with classifier free guidance with T5. This allows for one to do text-to-audio or TTS, not offered in the paper. Yes, this means VALL-E can be trained from this repository. It is essentially the same. This repository now also contains a MIT licensed version of SoundStream. It is also compatible with EnCodec, however, be aware that it has a more restrictive non-commercial license, if you choose to use it. -
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Awesome AI-ML-DL
Awesome Artificial Intelligence, Machine Learning and Deep Learning
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics. This repo is dedicated to engineers, developers, data scientists and all other professions that take interest in AI, ML, DL and related sciences. To make learning interesting and to create a place to easily find all the necessary material. Please contribute, watch, star, fork and share the repo with others in your community.