Search Results for "batch image processor"
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Showing 32 open source projects for "batch image processor"
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Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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AUTOMATIC1111 Stable Diffusion web UI
Stable Diffusion web UI
AUTOMATIC1111's stable-diffusion-webui is a powerful, user-friendly web interface built on the Gradio library that allows users to easily interact with Stable Diffusion models for AI-powered image generation. Supporting both text-to-image (txt2img) and image-to-image (img2img) generation, this open-source UI offers a rich feature set including inpainting, outpainting, attention control, and multiple advanced upscaling options. With a flexible installation process across Windows, Linux, and Apple Silicon, plus support for GPUs and CPUs, it caters to a wide range of users—from hobbyists to professionals. ... -
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GenAI Processors
GenAI Processors is a lightweight Python library
...The library offers built-in processors for classic turn-based Gemini calls as well as Live API streaming, so you can mix “batch” and real-time interactions in the same graph. It leans on Python’s asyncio to coordinate concurrency, handle network I/O, and juggle background compute threads without blocking. -
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ComfyUI Essentials
Essential nodes that are weirdly missing from ComfyUI core
ComfyUI_essentials is a ComfyUI custom node collection that adds practical nodes the author considers missing from the ComfyUI core. The project focuses on useful workflow building blocks rather than generic duplicates, with nodes for image handling, mask processing, sampling, segmentation, conditioning, text, and miscellaneous operations. Its image tools include functions for batching, cropping, flipping, resizing, compositing, background removal, color matching, LUT application, sharpening, tiling, and latent previewing. Its mask tools include blur, smoothing, fixing, flipping, color-based masks, segmentation masks, bounding boxes, transition masks, and batch utilities. ... -
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OpenAI Go
The official Go library for the OpenAI API
...It enables developers to integrate OpenAI’s models and features into Go applications with a clean and idiomatic interface. The library provides support for a wide range of API endpoints including chat completions, assistants, embeddings, image generation, audio processing, and batch jobs. It includes built-in tools for handling authentication, managing API requests, and parsing structured responses. The repository also offers examples to help developers quickly set up projects and test different API calls. Designed for reliability and ease of use, it is maintained to stay aligned with the evolving OpenAI API specifications. -
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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Cookbook (Google Gemini)
Examples and guides for using the Gemini API
...The repository covers a wide range of Gemini capabilities, including text, images, video, speech, robotics, and multimodal interactions. It highlights newly introduced features such as Gemini 2.5 models (Flash and Pro), Gemini’s native image generation, Veo for video generation, robotics-focused reasoning models, and Lyria for TTS and music generation. The Cookbook also includes tutorials on advanced API workflows such as grounding answers with external tools, batch-mode request handling, and live multimodal interactivity with LiveAPI. Designed as a hands-on resource, it helps developers quickly explore Gemini’s potential while serving as a reference for integrating cutting-edge multimodal AI into applications. -
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Markdown
WeChat Markdown Editor
WeChat Markdown Editor | A highly concise WeChat Markdown editor, that supports Markdown syntax, color palette selection, multi-image upload, one-click document download, custom CSS style, one-click reset, and other features. Markdown documents are automatically rendered into WeChat graphics and text in real-time, so you no longer have to worry about the typesetting of WeChat articles! As long as you know the basic Markdown syntax, you can make a simple and beautiful WeChat graphic. Chrome... -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
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SageMaker TensorFlow Training Toolkit
Toolkit for running TensorFlow training scripts on SageMaker
...To use your TensorFlow Serving model on SageMaker, you first need to create a SageMaker Model. After creating a SageMaker Model, you can use it to create SageMaker Batch Transform Jobs for offline inference, or create SageMaker Endpoints for real-time inference. A SageMaker Model contains references to a model.tar.gz file in S3 containing serialized model data, and a Docker image used to serve predictions with that model. A Batch Transform job runs an offline-inference job using your TensorFlow Serving model. ... -
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Benthos
Fancy stream processing made operationally mundane
Benthos is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads. It comes with a powerful mapping language, is easy to deploy and monitor, and ready to drop into your pipeline either as a static binary, docker image, or serverless function, making it cloud native as heck. -
Compliant and Reliable File Transfers Backed by Top Security CertificationsStop relying on non-certified, legacy file transfer tools that creak under the weight of modern security demands. Get full audit trails, advanced access controls and more supported by an award-winning team of experts. Start your free 25-day trial today.
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PicX
GitHub API
An image bed management tool with CDN acceleration function developed based on GitHub API. No need to download and install, use it online on the web page! free! Stablize! Convenient! Extremely fast! Writing articles with static blogs such as Hexo, VuePress, Hugo, etc., I don’t know where to save the pictures. Deliberately spending money to rent a cloud server to host pictures, it is too expensive and not worth it, and the upload configuration is cumbersome. The link to the favorite picture... -
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node-canvas
Node canvas is a Cairo backed Canvas implementation for NodeJS
...When MIME data is tracked, PDF canvases can embed JPEGs directly into the output, rather than re-encoding into PNG. This can drastically reduce filesize and speed up rendering. If working with a non-PDF canvas, image data must be tracked, otherwise the output will be junk. -
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SageMaker Spark Container
Docker image used to run data processing workloads
...It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker. -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
...These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations. -
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Simple Java Image Tool
Easy-to-use command line tool for images
SJIT is an easy-to-use command line program to analyze, manipulate and convert image files. -
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Neiki's Gallery
Vanilla JavaScript image gallery & lightbox
Neiki's Gallery is a lightweight, production-ready image gallery and lightbox library built with vanilla JavaScript and CSS. It requires no dependencies and can be integrated with a single <script> tag, with automatic initialization out of the box. It provides a highly customizable experience for modern web projects, combining performance, flexibility, and rich UI interactions. Designed for both developers and end users, it supports responsive layouts, advanced lightbox features, touch... -
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cerberuscms2
Cerberus Content Management System
Cerberus Content Management System is a dynamic, secure and infinitely expandable CMS designed after a Unix-Like model. It is a custom written Web Application Framework ( W.A.F. ) with a consistent and custom written Pre-Hyper-Text-Post-Processor Programming Code Framework ( P.C.F. ). This Web Application Software Project' aim is to be the fastest and most secure Web Application Framework, Web Application Programming Code Framework, Text, Voice and Video Communications Platform and Content... -
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Image Binder
Image Binder - Images-To-PDF Conversion
Introduction Welcome to Image Binder, a versatile application crafted for effortlessly converting and binding multiple images into a single PDF document. Whether you're organizing your image collection or creating professional portfolios, this tool provides a seamless solution with features designed to enhance your experience. -
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Portable Webserver OS For Small Intranet
For Web Developers, Testers, Learners, End Users - Screenshots Below
Instructions: 1. Download & Extract the above zip file 2. Use the free Rescuezilla Software ( https://rescuezilla.com/ ) to restore this pen drive image, on to a 8 GB OR More Good EMPTY Pen Drive (Hint Below) 3. Restart the computer and quickly change the BIOS Boot Order, to boot from this written pen drive 4. Check the network tray icon on its desktop for ethernet / lan cable connectivity. If required, configure by clicking the eth0 button in it, for the first time, after... -
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iJEPA
Official codebase for I-JEPA
i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. -
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QOR
Libraries written in Go abstracting features needed for business apps
QOR is architected from the ground up to accelerate development and deployment of Content Management Systems, E-commerce Systems, and Business Applications. QOR is comprised of modules that abstract common features for such systems, such as a configurable and flexible back office, a content publishing system, a media library, and much more. We built QOR at The Plant to accelerate development for our clients, it is at the core of our engineering strategy, and we maintain it on an ongoing... -
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Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
...The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. It emphasizes usability with utilities that handle alpha channels, gray/16-bit images, and tiled inference for large inputs, and can be run via Python scripts or portable executables. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll... -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
...Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. The Dockerfiles are grouped based on the version of TensorFlow Serving they support. Each supported processor type (e.g. "cpu", "gpu", "ei") has a different Dockerfile in each group. If your are testing locally, building the image is enough. But if you want to your updated image in SageMaker, you need to publish it to an ECR repository in your account. You can also run your container locally in Docker to test different models and input inference requests by hand. -
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This is a collection of libraries contained : Shared memory allocation, configuration script processor, containers (trees, lists, queues, stacks), file system abstraction, network library, timers/threads, Image library, Display libraries, and more.
