Search Results for "code blocks sample" - Page 2

Showing 92 open source projects for "code blocks sample"

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  • 1
    InfiniteYou

    InfiniteYou

    Flexible Photo Recrafting While Preserving Your Identity

    InfiniteYou is an open-source image-generation and “identity-preserving image editing / generation” framework from ByteDance, designed to generate high-fidelity images that preserve a subject’s identity while allowing flexible editing or re-creation according to textual prompts. Using an architecture built around diffusion transformers (DiTs), InfiniteYou introduces a component called InfuseNet that injects identity features derived from reference images into the generation process — via...
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  • 2
    Denoising Diffusion Probabilistic Model

    Denoising Diffusion Probabilistic Model

    Implementation of Denoising Diffusion Probabilistic Model in Pytorch

    Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model.
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  • 3
    Transformer Engine

    Transformer Engine

    A library for accelerating Transformer models on NVIDIA GPUs

    Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. TE also includes a framework-agnostic C++ API that can be integrated with other deep-learning libraries to enable FP8 support for Transformers. As the number of parameters in Transformer models continues to grow, training and inference for architectures such as BERT, GPT, and T5 become very memory and compute-intensive. ...
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  • 4
    EKS Best Practices

    EKS Best Practices

    A best practices guide for day 2 operations

    The Amazon EKS Best Practices Guide is a public repository containing comprehensive documentation and guidance for operating production-grade Kubernetes clusters on AWS’s managed service, Amazon EKS. Rather than a code library, it serves as a reference catalogue of patterns, anti-patterns, checklists and architectures across domains such as security, reliability, scalability, networking, cost optimization and hybrid cloud deployments. The repository is maintained by AWS but open to...
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  • 5
    Gemma in PyTorch

    Gemma in PyTorch

    The official PyTorch implementation of Google's Gemma models

    ...The repository demonstrates text generation pipelines, tokenizer setup, quantization paths, and adapters for low-rank or parameter-efficient fine-tuning. Example notebooks walk through instruction tuning and evaluation so teams can benchmark and iterate rapidly. The code is organized to be legible and hackable, exposing attention blocks, positional encodings, and head configurations. With standard PyTorch abstractions, it integrates easily into existing training loops, loggers, and evaluation harnesses.
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  • 6
    TypeAgent Python

    TypeAgent Python

    Structured RAG: ingest, index, query

    ...This design allows the system to combine the flexibility of language models with the reliability of traditional programming logic. The repository is intended primarily as a research prototype and sample code rather than a production-ready framework, allowing developers to experiment with building AI agents that maintain structured memory and perform tasks through defined actions.
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  • 7
    Open Model Zoo

    Open Model Zoo

    Pre-trained Deep Learning models and demos

    Open Model Zoo is a large repository of high-quality pre-trained deep learning models and demonstration applications designed to work with the OpenVINO™ toolkit, offering a comprehensive starting point for a wide range of AI and computer vision workloads. It includes hundreds of models covering object detection, classification, segmentation, pose estimation, speech recognition, text-to-speech, and more, many of which are already converted into formats optimized for inference on CPUs, GPUs,...
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  • 8
    DreamCraft3D

    DreamCraft3D

    Official implementation of DreamCraft3D

    ...The name suggests a “dream crafting” metaphor—users probably supply textual or image prompts and generate 3D assets (point clouds, meshes, scenes). The repository includes model code, inference scripts, sample prompts, and possibly dataset preparation pipelines. It may integrate rendering or post-processing modules (e.g. mesh smoothing, texturing) to make the outputs more output-ready. Because 3D generation is hardware‐intensive, the repository likely also includes optimizations like quantization, pruning, or inference accelerations (e.g. using FlashMLA or DeepEP) to make the generation pipeline faster or more efficient. ...
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  • 9
    Dia

    Dia

    A TTS model capable of generating ultra-realistic dialogue

    ...Instead of focusing on isolated sentences or flat narration, it is optimized for conversational audio, complete with natural turn-taking, prosody, and pacing. The model can be conditioned on a reference audio sample, allowing you to control emotion, tone, and other stylistic aspects of the speech. It can also produce nonverbal vocalizations like laughter, coughs, clearing the throat, and similar sounds, which are crucial for making synthetic conversations feel human. Dia is released with pretrained checkpoints and inference code, with weights hosted on Hugging Face, so researchers and developers can quickly try it or integrate it into pipelines. ...
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  • 10
    fastai

    fastai

    Deep learning library

    fastai is a deep learning library which provides practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains, and provides researchers with low-level components that can be mixed and matched to build new approaches. It aims to do both things without substantial compromises in ease of use, flexibility, or performance. This is possible thanks to a carefully layered architecture, which expresses common underlying...
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  • 11
    Curated Transformers

    Curated Transformers

    PyTorch library of curated Transformer models and their components

    State-of-the-art transformers, brick by brick. Curated Transformers is a transformer library for PyTorch. It provides state-of-the-art models that are composed of a set of reusable components. Supports state-of-the-art transformer models, including LLMs such as Falcon, Llama, and Dolly v2. Implementing a feature or bugfix benefits all models. For example, all models support 4/8-bit inference through the bitsandbytes library and each model can use the PyTorch meta device to avoid unnecessary...
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  • 12
    OpenCV

    OpenCV

    Open Source Computer Vision Library

    The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials! ...
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    Downloads: 17,806 This Week
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  • 13

    rx2fits

    HF/VHF spectrosopy code for the rx888mk2 direct-sampling receiver

    rx2fits is an SDR code for the rx888mk2 direct-sampling receiver that streams spectral data with frequency up to 65 MHz to FITS files. This code is for the direct-sampling input of the receiver, which transmits real samples over USB3 at up to 130 MHz sample rate. rx2fits processes these samples to spectral intensities via a Fourier-transform poly-phase filter bank, which provides spectral resolution approaching the spectral bin width with good stop-band and adjacent-channel rejection. ...
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  • 14
    dxf2gcode

    dxf2gcode

    DXF2GCODE: converting 2D dxf drawings to CNC machine compatible G-Code

    DXF2GCODE is a tool for converting 2D (dxf, pdf, ps) drawings to CNC machine compatible GCode. Windows, Linux, and Mac support by using python scripting language.
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    Downloads: 366 This Week
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  • 15
    KherveFitting

    KherveFitting

    Fit X-ray Photoelectron Spectroscopy (XPS) and XAS data

    KherveFitting is a full featured XPS, XAS and Raman fitting software for Windows & macOS that is simple and easy to use. KherveFitting can open Excel files (.xlsx) or import/convert .vms, .avg, .kal, .spe (Thermo, Kratos, Specs, Scienta, Omicron, Phi) files into Excel format. 5 background types available: Tougaard, Shirley, Smart, Adaptive Smart. Various peak fitting models: Product and Sum of Gaussian and Lorentzian (GL & SGL), Voigt, Asymmetric Lorentzian (LA), Doniac-Sunjic (G*DS) and...
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    Downloads: 215 This Week
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  • 16
    Muse: Middleware Universal Scripting idE

    Muse: Middleware Universal Scripting idE

    Automate: WebSphere; WebLogic; JBoss; Glassfish; Tomcat; Linux, WinRM

    Simplify... Aggregate... Automate... Simplify... *** OPEN SOURCE - GPL3/EPL. Use Python / Jython to automate WebSphere, WebLogic, JBoss, Glassfish and Tomcat Middleware Estates over JMX, both SSL and non-SSL + Linux SSH (agent-less) + WinRM Target all 5 servers, Linux and WinRM from the same workspace. Familiar Eclipse based Jython and Python Development IDE, pre-configured and ready to go. 4-Click Installer. Win x64, Linux WINE x64. Built-In JVM. Java 8/9/10, Amazon...
    Downloads: 1 This Week
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  • 17
    Links Into Social Media Posts

    Links Into Social Media Posts

    Provide a list of links, get back a CSV of social media post drafts

    ...It did all that magic automatically and without AI! (Source code is included with download in 'src' folder!")
    Downloads: 0 This Week
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  • 18
    Shap-E

    Shap-E

    Generate 3D objects conditioned on text or images

    ...Because it works at the level of implicit functions, Shap-E can render output both as textured meshes and NeRF-style volumetric renderings. The repository contains sample notebooks (e.g. sample_text_to_3d.ipynb, sample_image_to_3d.ipynb) so users can try out text → 3D or image → 3D generation. The code is distributed under the MIT license, and includes a “model card” that documents limitations, recommended use, and ethical considerations.
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  • 19

    Taylorplot_Neptune

    Creation of a Taylorplot for several machine learning models

    Here we present the lines of code for creating a taylor plot with python to display several machine learning models. We show the solution for displaying 10 models, but the list and number can be changed simply by modifying the sample list.
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  • 20
    Asteroid

    Asteroid

    The PyTorch-based audio source separation toolkit for researchers

    The PyTorch-based audio source separation toolkit for researchers. Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code thats supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers. Building blocks are thought and designed to be seamlessly plugged together. Filterbanks, encoders, maskers, decoders and losses are all common building blocks that can be combined in a flexible way to create new systems. ...
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  • 21
    ComfyUI Experiments

    ComfyUI Experiments

    Some experimental custom nodes

    ...The project is aimed at power users and contributors who want to see “what’s possible” with ComfyUI beyond the stable set of features. Because it is exploratory, the code may change often, rely on specific versions, or require manual setup, which is why it’s separated from the main ComfyUI codebase. It also serves as an inspiration library: people can study experimental graphs and adapt the logic to their own local workflows. In short, it’s the R&D corner of ComfyUI where new building blocks are tested in the open.
    Downloads: 1 This Week
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  • 22
    DiT (Diffusion Transformers)

    DiT (Diffusion Transformers)

    Official PyTorch Implementation of "Scalable Diffusion Models"

    DiT (Diffusion Transformer) is a powerful architecture that applies transformer-based modeling directly to diffusion generative processes for high-quality image synthesis. Unlike CNN-based diffusion models, DiT represents the diffusion process in the latent space and processes image tokens through transformer blocks with learned positional encodings, offering scalability and superior sample quality. The model architecture parallels large language models but for image tokens—each block refines noisy latent representations toward cleaner outputs through iterative denoising steps. DiT achieves strong results on benchmarks like ImageNet and LSUN while being architecturally simple and highly modular. ...
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  • 23
    RPA for Python

    RPA for Python

    Python package for doing RPA

    ...TagUI is a leading open-source RPA software with tens of thousands of users. It was created in 2016-2017 when I left DBS Bank as a test automation engineer, for a one-year sabbatical to Eastern Europe. Most of its code base was written in Novi Sad Serbia. In 2018, I joined AI Singapore to continue development of TagUI.
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  • 24
    lora-svc

    lora-svc

    Singing voice change based on whisper, lora for singing voice clone

    singing voice change based on whisper, and lora for singing voice clone. You will feel the beauty of the code from this project. Uni-SVC main branch is for singing voice clone based on whisper with speaker encoder and speaker adapter. Uni-SVC main target is to develop lora for SVC. With lora, maybe clone a singer just need 10 stence after 10 minutes train. Each singer is a plug-in of the base model.
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  • 25
    minGPT

    minGPT

    A minimal PyTorch re-implementation of the OpenAI GPT

    minGPT is a minimalist, educational re-implementation of the GPT (Generative Pretrained Transformer) architecture built in PyTorch, designed by Andrej Karpathy to expose the core structure of a transformer-based language model in as few lines of code as possible. It strips away extraneous bells and whistles, aiming to show how a sequence of token indices is fed into a stack of transformer blocks and then decoded into the next token probabilities, with both training and inference supported. Because the whole model is around 300 lines of code, users can follow each step—from embedding lookup, positional encodings, multi-head attention, feed-forward layers, to output heads—and thus demystify how GPT-style models work beneath the surface. ...
    Downloads: 1 This Week
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