Search Results for "artificial" - Page 2
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Showing 133 open source projects for "artificial"
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Deep Lake
Data Lake for Deep Learning. Build, manage, and query datasets
Deep Lake (or Deeplake, formerly known as Activeloop Hub) is a data lake for deep learning applications. Our open-source dataset format is optimized for rapid streaming and querying of data while training models at scale, and it includes a simple API for creating, storing, and collaborating on AI datasets of any size. It can be deployed locally or in the cloud, and it enables you to store all of your data in one place, ranging from simple annotations to large videos. Deep Lake is used by... -
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Simple StyleGan2 for Pytorch
Simplest working implementation of Stylegan2
Simple Pytorch implementation of Stylegan2 that can be completely trained from the command-line, no coding needed. You will need a machine with a GPU and CUDA installed. You can also specify the location where intermediate results and model checkpoints should be stored. You can increase the network capacity (which defaults to 16) to improve generation results, at the cost of more memory. By default, if the training gets cut off, it will automatically resume from the last checkpointed file.... -
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DocsGPT
Private AI platform for agents, enterprise search and RAG pipelines
DocsGPT is an open-source AI platform for deploying private RAG pipelines, AI agents, and enterprise search on your own infrastructure. Connect any data source (PDFs, DOCX, CSV, Excel, HTML, audio, GitHub, databases, URLs) and get accurate, hallucination-free answers with source citations. Choose your LLM: OpenAI, Anthropic, Google Gemini, or local models. Works with Qdrant, MongoDB, and Elasticsearch and more. Deploy via Docker or Kubernetes with full data sovereignty. Build... -
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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... -
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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... -
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Kaleidoscope-SDK
User toolkit for analyzing and interfacing with Large Language Models
kaleidoscope-sdk is a Python module used to interact with large language models hosted via the Kaleidoscope service available at: https://github.com/VectorInstitute/kaleidoscope. It provides a simple interface to launch LLMs on an HPC cluster, asking them to perform basic features like text generation, but also retrieve intermediate information from inside the model, such as log probabilities and activations. Users must authenticate using their Vector Institute cluster credentials. This can... -
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KoboldCpp
Run GGUF models easily with a UI or API. One File. Zero Install.
KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, inspired by the original KoboldAI. It's a single self-contained distributable that builds off llama.cpp and adds many additional powerful features. -
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dashAI
dashAI: an interactive platform for training, evaluating and deploying
dashAI is an open-source, No-code workbench for Exploratory Data Analysis and classical ML. Visual data preparation, multi-model experiments, XAI explainability, and a plugin-based extensible catalog. The platform guides users through a complete, traceable workflow — data ingestion → visual exploration → preprocessing → model training → evaluation → explainability — without writing a single line of code. Each step is explicit and reversible, keeping the user in control rather than... -
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x-unet
Implementation of a U-net complete with efficient attention
Implementation of a U-net complete with efficient attention as well as the latest research findings. For 3d (video or CT / MRI scans). -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Make-A-Video - Pytorch (wip)
Implementation of Make-A-Video, new SOTA text to video generator
Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch. They combine pseudo-3d convolutions (axial convolutions) and temporal attention and show much better temporal fusion. The pseudo-3d convolutions isn't a new concept. It has been explored before in other contexts, say for protein contact prediction as "dimensional hybrid residual networks". The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning... -
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website-to-gif
Turn your website into a GIF
This Github Action automatically creates an animated GIF or WebP from a given web page to display on your project README (or anywhere else). In your GitHub repo, create a workflow file or extend an existing one. You have to also include a step to checkout and commit to the repo. You can use the following example gif.yml. Make sure to modify the url value and add any other input you want to use. WebP rendering will take a lot of time to benefit from lossless quality and file size... -
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Video Diffusion - Pytorch
Implementation of Video Diffusion Models
Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch. Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch. It uses a special space-time factored U-net, extending generation from 2D images to 3D videos. 14k for difficult moving mnist (converging much faster and better than NUWA) - wip. Any new developments for text-to-video synthesis will be centralized at... -
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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... -
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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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ChatGPT-Reviewer
Automated pull requests reviewing and issues triaging with ChatGPT
Automated pull requests reviewing and issues triaging with ChatGPT. Create an OpenAI API key here, and then set the key as an action secret in your repository named OPENAI_API_KEY. The ChatGPT reviewer PRs are also getting reviewed by ChatGPT, refer the pull requests for the sample review comments. In order to protect public repositories for malicious users, Github runs all pull request workflows raised from repository forks with a read-only token and no access to secrets. -
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Shap-E
Generate 3D objects conditioned on text or images
The shap-e repository provides the official code and model release for Shap-E, a conditional generative model designed to produce 3D assets (implicit functions, meshes, neural radiance fields) from text or image prompts. The model is built with a two-stage architecture: first an encoder that maps existing 3D assets into parameterizations of implicit functions, and then a conditional diffusion model trained on those parameterizations to generate new assets. Because it works at the level of... -
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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... -
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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... -
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DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as... -
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ChatFred
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting, image generation and more. Access ChatGPT, DALL·E 2, and other OpenAI models. Language models often give wrong information. Verify answers if they are important. Talk with ChatGPT via the cf keyword. Answers will show as Large Type. Alternatively, use the Universal Action, Fallback Search, or Hotkey. To generate text with InstructGPT models and see results in-line, use the cft keyword. ⤓ Install on the Alfred Gallery or... -
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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... -
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MusicLM - Pytorch
Implementation of MusicLM music generation model in Pytorch
Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch. They are basically using text-conditioned AudioLM, but surprisingly with the embeddings from a text-audio contrastive learned model named MuLan. MuLan is what will be built out in this repository, with AudioLM modified from the other repository to support the music generation needs here. -
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Petals
Run 100B+ language models at home, BitTorrent-style
Run 100B+ language models at home, BitTorrent‑style. Run large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. Single-batch inference runs at ≈ 1 sec per step (token) — up to 10x faster than offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec. Beyond classic language model APIs — you can employ any fine-tuning and... -
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Basaran
Basaran, an open-source alternative to the OpenAI text completion API
Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models. The open source community will eventually witness the Stable Diffusion moment for large language models (LLMs), and Basaran allows you to replace OpenAI's service with the latest open-source model to power your application without modifying a single line of code. Stream generation using various decoding strategies.... -
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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...
