Search Results for "e-root" - Page 2
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Showing 43 open source projects for "e-root"
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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.
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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. -
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VALL-E X
Open source implementation of Microsoft's VALL-E X zero-shot TTS model
VALL-E-X is an open-source implementation of Microsoft’s VALL-E X zero-shot text-to-speech model, focused on multilingual, cross-lingual voice cloning. It is capable of synthesizing speech in English, Chinese, and Japanese from text while mimicking the voice characteristics of a speaker given only a short 3–10 second prompt. The model attempts to match not just timbre, but also tone, pitch, emotion, and prosody of the reference audio, resulting in highly personalized output. ... -
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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 the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. ... -
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State of Open Source AI
Clarity in the current fast-paced mess of Open Source innovation
...The structure includes chapters or sections about model formats, evaluation benchmarks, hardware/backends, MLOps systems, alignment and safety issues, and open datasets. The repository contains the text (in Markdown or similar), configuration for build or publishing (static site or e-book), and contributor guidelines. -
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DALL-E in Pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image
Implementation / replication of DALL-E (paper), OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the generations. Kobiso, a research engineer from Naver, has trained on the CUB200 dataset here, using full and deepspeed sparse attention. You can also skip the training of the VAE altogether, using the pretrained model released by OpenAI! -
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macara
A converter for seamless transformation of files, data, and media ...
...Serving as a versatile tool, it facilitates efficient file management, especially when handling a substantial volume of images, whether sorting by name or other attributes. These scripts are crafted to complement generative art AI technologies like Dall-e or stable diffusion. -
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VALL-E
PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)
...In addition, we find VALL-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. -
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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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ruDALL-E
Generate images from texts. In Russian
...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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Alphafold2
Unofficial Pytorch implementation / replication of Alphafold2
...The underlying assumption is that the trunk works on the residue level, and then constitutes to atomic level for the structure module, whether it be SE3 Transformers, E(n)-Transformer, or EGNN doing the refinement. -
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AI Wallpapers
Change your wallpaper daily using images generated with DALL-E 2
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e-Dokyumento
e-Dokyumento is web-based Document Management System (DMS)
.... # Demo : https://e-dokyumento.herokuapp.com/ https://edokyu.seillig.com/ (refer to Readme.md for the accounts) #Dockerhub: https://hub.docker.com/r/nelsonmaligro/edokyumento # Install using the ISO: 1. Download: https://sourceforge.net/projects/e-dokyumento/files/Releases/e-DokyuV3.iso/download 2. Boot and login with: "root" and "admin@123" 3. -
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SageMaker Scikit-Learn Extension
A library of additional estimators and SageMaker tools based on scikit
...The mlio package is only available through conda at the moment. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. For unit tests, tox will use pytest to run the unit tests in a Python 3.7 interpreter. tox will also run flake8 and pylint for style checks. -
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Minkowski Engine
Auto-diff neural network library for high-dimensional sparse tensors
...The Minkowski Engine supports various functions that can be built on a sparse tensor. We list a few popular network architectures and applications here. To run the examples, please install the package and run the command in the package root directory. Compressing a neural network to speed up inference and minimize memory footprint has been studied widely. One of the popular techniques for model compression is pruning the weights in convnets, is also known as sparse convolutional networks. Such parameter-space sparsity used for model compression compresses networks that operate on dense tensors and all intermediate activations of these networks are also dense tensors. -
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual... -
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e-Metis - ML
Modul za napovedovanje učnih težav.
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Turn your scanner into a free document reader for invoices (e.g. for e-banking) with the help of tesseract-ocr available for many unix (and also windows) platforms.
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