Best AI tools for< Build Image >

SentiSight.ai is a machine learning platform for image recognition solutions, offering services such as object detection, image segmentation, image classification, image similarity search, image annotation, computer vision consulting, and intelligent automation consulting. Users can access pre-trained models, background removal, NSFW detection, text recognition, and image recognition API. The platform provides tools for image labeling, project management, and training tutorials for various image recognition models. SentiSight.ai aims to streamline the image annotation process, empower users to build and train their own models, and deploy them for online or offline use.

PhotoTravel AI is an innovative AI application that allows users to take photos of themselves at famous landmarks worldwide without physically traveling to those locations. Users can upload their images to build their AI model, which then generates realistic photos of them at iconic tourist spots. The application provides an affordable and convenient alternative to traditional travel, enabling users to create and share memories from the comfort of their homes.

Imagga is a leading provider of image recognition solutions for developers and businesses. Its API empowers intelligent apps with customizable machine learning technology. Imagga's solutions include tagging, categorization, cropping, color extraction, visual search, facial recognition, custom training, and content moderation. These solutions are used by over 30K startups, developers, and students, and trusted by over 200 business customers in more than 82 countries worldwide.

Roboflow is a platform that provides tools for building and deploying computer vision models. It offers a range of features, including data annotation, model training, and deployment. Roboflow is used by over 250,000 engineers to create datasets, train models, and deploy to production.

Midjourney Prompt Generator is a tool that helps users create prompts for Midjourney, an AI-powered image generation bot. It provides a user-friendly interface for setting parameters, applying style presets, and weighting parts of the prompt. The tool also allows users to save and share their prompts. Midjourney Prompt Generator is a valuable resource for anyone who wants to get the most out of Midjourney.

Create is a free-to-use AI app builder that lets you code using plain text and images. With Create, you can design and build apps like a pro, without having to write a single line of code. Create is perfect for building internal tools, prototypes, and even full-fledged applications.

Toolmark.ai is a no-code AI tool builder that allows users to create AI tools without any coding knowledge. With Toolmark.ai, users can build AI tools that generate text, images, voice, and more using GPT, Dall-E, Google Gemini, and other AI models. Toolmark.ai also offers a marketplace where creators can design and sell their AI tools.

CGDream is an AI image generator that allows users to visualize their ideas by generating images from text prompts. It offers various features such as text-to-image, image-to-image, and 3D model-to-image generation. Users can also apply filters to enhance the quality and style of the generated images. The tool is particularly useful for creative professionals, designers, and anyone looking to explore their imagination and bring their ideas to life.

Clevis is a platform that allows users to build and sell AI-powered applications without the need for coding. With a variety of pre-built processing steps, users can create apps with features like text generation, image generation, and web scraping. Clevis provides an easy-to-use editor, templates, sharing options, and API integration to help users launch their AI-powered apps quickly. The platform also offers customization options, data upload capabilities, and the ability to schedule app runs. Clevis offers a free trial period and flexible pricing plans to cater to different user needs.

Sink In is a cloud-based platform that provides access to Stable Diffusion AI image generation models. It offers a variety of models to choose from, including majicMIX realistic, MeinaHentai, AbsoluteReality, DreamShaper, and more. Users can generate images by inputting text prompts and selecting the desired model. Sink In charges $0.0015 for each 512x512 image generated, and it offers a 99.9% reliability guarantee for images generated in the last 30 days.

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

SitesGPT is a premier AI Website Builder that leverages Artificial Intelligence (AI) technology to revolutionize website creation. It offers a user-friendly platform where individuals and businesses can effortlessly build dynamic, responsive websites with just a few clicks. With features like mobile optimization, unparalleled flexibility, zero cost to start, robust cloud infrastructure, and round-the-clock operation, SitesGPT stands out as a cost-effective and efficient solution for website development. The fusion of AI and website building not only enhances speed and efficiency but also ensures scalability and customization, making professional website creation accessible to a broader audience.

BRIA.ai is a visual generative AI platform that provides developers and businesses with the tools they need to build and deploy AI-powered applications. The platform includes a suite of pre-trained foundation models, APIs, and tools that can be used to generate and modify images, videos, and other visual content. BRIA.ai is committed to responsible AI practices and ensures that all of its models are trained on licensed and safe-to-use data.

Meteron AI is an all-in-one AI toolset that helps developers build AI-powered products faster and easier. It provides a simple, yet powerful metering mechanism, elastic scaling, unlimited storage, and works with any model. With Meteron, developers can focus on building AI products instead of worrying about the underlying infrastructure.

Gradio is a tool that allows users to quickly and easily create web-based interfaces for their machine learning models. With Gradio, users can share their models with others, allowing them to interact with and use the models remotely. Gradio is easy to use and can be integrated with any Python library. It can be used to create a variety of different types of interfaces, including those for image classification, natural language processing, and time series analysis.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

Massed Compute is an AI tool that provides cloud GPU services for VFX rendering, machine learning, high-performance computing, scientific simulations, and data analytics & visualization. The platform offers flexible and affordable plans, cutting-edge technology infrastructure, and timely creative problem-solving. As an NVIDIA Preferred Partner, Massed Compute ensures reliable and future-proof Tier III Data Center servers for various computing needs. Users can launch AI instances, scale machine learning projects, and access high-performance GPUs on-demand.

Leap AI is a no-code AI workflow automation platform that enables businesses to automate tasks, streamline processes, and enhance productivity. With Leap AI, users can easily connect their favorite tools and leverage best-in-class AI models for text, image, audio, and more. The platform offers a range of features designed to accelerate growth, including effortless automation, seamless AI integrations, zero maintenance, streamlined collaboration, and complete control and flexibility.

Hocoos AI Website Builder is a powerful tool that allows users to create professional business websites in minutes. The AI builder simplifies the website creation process by asking 8 simple questions, generating unique designs tailored to the business, and building a perfect website quickly. In addition to website building, Hocoos offers a suite of AI tools including an AI Logo Generator, AI Image Editor, and AI Content Generator to enhance the website's visuals and content. The platform aims to empower users with no design or coding skills to establish a strong online presence effortlessly.

Gemini is a large and powerful AI model developed by Google. It is designed to handle a wide variety of text and image reasoning tasks, and it can be used to build a variety of AI-powered applications. Gemini is available in three sizes: Ultra, Pro, and Nano. Ultra is the most capable model, but it is also the most expensive. Pro is the best performing model for a wide variety of tasks, and it is a good value for the price. Nano is the most efficient model, and it is designed for on-device use cases.

This repository contains scripts for installing HPC and AI libraries and tools to build Azure HPC/AI images. It streamlines the process of provisioning compute-intensive workloads and crafting advanced AI models in the cloud, ensuring efficiency and reliability in deployments.

ztachip is a RISCV accelerator designed for vision and AI edge applications, offering up to 20-50x acceleration compared to non-accelerated RISCV implementations. It features an innovative tensor processor hardware to accelerate various vision tasks and TensorFlow AI models. ztachip introduces a new tensor programming paradigm for massive processing/data parallelism. The repository includes technical documentation, code structure, build procedures, and reference design examples for running vision/AI applications on FPGA devices. Users can build ztachip as a standalone executable or a micropython port, and run various AI/vision applications like image classification, object detection, edge detection, motion detection, and multi-tasking on supported hardware.

Translate texts in manga/images. Some manga/images will never be translated, therefore this project is born. * Image/Manga Translator * Samples * Online Demo * Disclaimer * Installation * Pip/venv * Poetry * Additional instructions for **Windows** * Docker * Hosting the web server * Using as CLI * Setting Translation Secrets * Using with Nvidia GPU * Building locally * Usage * Batch mode (default) * Demo mode * Web Mode * Api Mode * Related Projects * Docs * Recommended Modules * Tips to improve translation quality * Options * Language Code Reference * Translators Reference * GPT Config Reference * Using Gimp for rendering * Api Documentation * Synchronous mode * Asynchronous mode * Manual translation * Next steps * Support Us * Thanks To All Our Contributors :

Instill Core is an open-source orchestrator comprising a collection of source-available projects designed to streamline every aspect of building versatile AI features with unstructured data. It includes Instill VDP (Versatile Data Pipeline) for unstructured data, AI, and pipeline orchestration, Instill Model for scalable MLOps and LLMOps for open-source or custom AI models, and Instill Artifact for unified unstructured data management. Instill Core can be used for tasks such as building, testing, and sharing pipelines, importing, serving, fine-tuning, and monitoring ML models, and transforming documents, images, audio, and video into a unified AI-ready format.

horde-engine is a wrapper around ComfyUI designed to run inference pipelines visually designed in the ComfyUI GUI. It enables users to design inference pipelines in ComfyUI and then call them programmatically, maintaining compatibility with the existing horde implementation. The library provides features for processing Horde payloads, initializing the library, downloading and validating models, and generating images based on input data. It also includes custom nodes for preprocessing and tasks such as face restoration and QR code generation. The project depends on various open source projects and bundles some dependencies within the library itself. Users can design ComfyUI pipelines, convert them to the backend format, and run them using the run_image_pipeline() method in hordelib.comfy.Comfy(). The project is actively developed and tested using git, tox, and a specific model directory structure.

SuperBench is a validation and profiling tool for AI infrastructure. It provides a comprehensive set of tests and benchmarks to evaluate the performance and reliability of AI systems. The tool helps users identify bottlenecks, optimize configurations, and ensure the stability of their AI infrastructure. SuperBench is designed to streamline the validation process and improve the overall efficiency of AI deployments.

PyTorch Forecasting is a PyTorch-based package for time series forecasting with state-of-the-art network architectures. It offers a high-level API for training networks on pandas data frames and utilizes PyTorch Lightning for scalable training on GPUs and CPUs. The package aims to simplify time series forecasting with neural networks by providing a flexible API for professionals and default settings for beginners. It includes a timeseries dataset class, base model class, multiple neural network architectures, multi-horizon timeseries metrics, and hyperparameter tuning with optuna. PyTorch Forecasting is built on pytorch-lightning for easy training on various hardware configurations.

Züs is a high-performance cloud on a fast blockchain offering privacy and configurable uptime. It uses erasure code to distribute data between data and parity servers, allowing flexibility for IT managers to design for security and uptime. Users can easily share encrypted data with business partners through a proxy key sharing protocol. The ecosystem includes apps like Blimp for cloud migration, Vult for personal cloud storage, and Chalk for NFT artists. Other apps include Bolt for secure wallet and staking, Atlus for blockchain explorer, and Chimney for network participation. The QoS protocol challenges providers based on response time, while the privacy protocol enables secure data sharing. Züs supports hybrid and multi-cloud architectures, allowing users to improve regulatory compliance and security requirements.

Bolna is an open-source platform for building voice-driven conversational applications using large language models (LLMs). It provides a comprehensive set of tools and integrations to handle various aspects of voice-based interactions, including telephony, transcription, LLM-based conversation handling, and text-to-speech synthesis. Bolna simplifies the process of creating voice agents that can perform tasks such as initiating phone calls, transcribing conversations, generating LLM-powered responses, and synthesizing speech. It supports multiple providers for each component, allowing users to customize their setup based on their specific needs. Bolna is designed to be easy to use, with a straightforward local setup process and well-documented APIs. It is also extensible, enabling users to integrate with other telephony providers or add custom functionality.

Melodisco is an AI music player that allows users to listen to music and manage playlists. It provides a user-friendly interface for music playback and organization. Users can deploy Melodisco with Vercel or Docker for easy setup. Local development instructions are provided for setting up the project environment. The project credits various tools and libraries used in its development, such as Next.js, Tailwind CSS, and Stripe. Melodisco is a versatile tool for music enthusiasts looking for an AI-powered music player with features like authentication, payment integration, and multi-language support.

The bilingual_book_maker is an AI translation tool that uses ChatGPT to assist users in creating multi-language versions of epub/txt/srt files and books. It supports various models like gpt-4, gpt-3.5-turbo, claude-2, palm, llama-2, azure-openai, command-nightly, and gemini. Users need ChatGPT or OpenAI token, epub/txt books, internet access, and Python 3.8+. The tool provides options to specify OpenAI API key, model selection, target language, proxy server, context addition, translation style, and more. It generates bilingual books in epub format after translation. Users can test translations, set batch size, tweak prompts, and use different models like DeepL, Google Gemini, Tencent TranSmart, and more. The tool also supports retranslation, translating specific tags, and e-reader type specification. Docker usage is available for easy setup.

GitHub Sentinel is an intelligent information retrieval and high-value content mining AI Agent designed for the era of large models (LLMs). It is aimed at users who need frequent and large-scale information retrieval, especially open source enthusiasts, individual developers, and investors. The main features include subscription management, update retrieval, notification system, report generation, multi-model support, scheduled tasks, graphical interface, containerization, continuous integration, and the ability to track and analyze the latest dynamics of GitHub open source projects and expand to other information channels like Hacker News for comprehensive information mining and analysis capabilities.

This repository, llamafile-docker, automates the process of checking for new releases of Mozilla-Ocho/llamafile, building a Docker image with the latest version, and pushing it to Docker Hub. Users can download a pre-trained model in gguf format and use the Docker image to interact with the model via a server or CLI version. Contributions are welcome under the Apache 2.0 license.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

The worker-vLLM repository provides a serverless endpoint for deploying OpenAI-compatible vLLM models with blazing-fast performance. It supports deploying various model architectures, such as Aquila, Baichuan, BLOOM, ChatGLM, Command-R, DBRX, DeciLM, Falcon, Gemma, GPT-2, GPT BigCode, GPT-J, GPT-NeoX, InternLM, Jais, LLaMA, MiniCPM, Mistral, Mixtral, MPT, OLMo, OPT, Orion, Phi, Phi-3, Qwen, Qwen2, Qwen2MoE, StableLM, Starcoder2, Xverse, and Yi. Users can deploy models using pre-built Docker images or build custom images with specified arguments. The repository also supports OpenAI compatibility for chat completions, completions, and models, with customizable input parameters. Users can modify their OpenAI codebase to use the deployed vLLM worker and access a list of available models for deployment.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

Flowise is a tool that allows users to build customized LLM flows with a drag-and-drop UI. It is open-source and self-hostable, and it supports various deployments, including AWS, Azure, Digital Ocean, GCP, Railway, Render, HuggingFace Spaces, Elestio, Sealos, and RepoCloud. Flowise has three different modules in a single mono repository: server, ui, and components. The server module is a Node backend that serves API logics, the ui module is a React frontend, and the components module contains third-party node integrations. Flowise supports different environment variables to configure your instance, and you can specify these variables in the .env file inside the packages/server folder.

Midjourney-proxy is a proxy for the Discord channel of MidJourney, enabling API-based calls for AI drawing. It supports Imagine instructions, adding image base64 as a placeholder, Blend and Describe commands, real-time progress tracking, Chinese prompt translation, prompt sensitive word pre-detection, user-token connection to WSS, multi-account configuration, and more. For more advanced features, consider using midjourney-proxy-plus, which includes Shorten, focus shifting, image zooming, local redrawing, nearly all associated button actions, Remix mode, seed value retrieval, account pool persistence, dynamic maintenance, /info and /settings retrieval, account settings configuration, Niji bot robot, InsightFace face replacement robot, and an embedded management dashboard.

Jina is a tool that allows users to build multimodal AI services and pipelines using cloud-native technologies. It provides a Pythonic experience for serving ML models and transitioning from local deployment to advanced orchestration frameworks like Docker-Compose, Kubernetes, or Jina AI Cloud. Users can build and serve models for any data type and deep learning framework, design high-performance services with easy scaling, serve LLM models while streaming their output, integrate with Docker containers via Executor Hub, and host on CPU/GPU using Jina AI Cloud. Jina also offers advanced orchestration and scaling capabilities, a smooth transition to the cloud, and easy scalability and concurrency features for applications. Users can deploy to their own cloud or system with Kubernetes and Docker Compose integration, and even deploy to JCloud for autoscaling and monitoring.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

Unique custom images based on your input. Just type ideas and the brand image is created.

Welcome to 'SelfEsteemSculptor' – your digital guide for enhancing self-esteem. Discover strategies to build your confidence, overcome self-doubt, and cultivate a strong, positive self-image.

Transforms ideas into Lego creations, keeps creation details private.

Cinematic Image Design Assistant

I generate concise Chakra UI code from UI images or requirements.

A guide for self-sufficiency and nature awareness, with internet search and image generation.

A data assistant for devs, now with real video and image links.

根据小红书正文内容生成封面图

上传一张线框图生成前端HTML代码

Expert in full-scale app development, coding, and OCR.

You are a helpful Visual Synthesizer assistant.

The most advanced API finder, available for over 2000 manually curated tasks. Chat with me to find the best AI tools for any use case.

Creates thematic stock photos for slideshows, websites or print media.

Enter a richly detailed steampunk universe in 'SteamMaster: Inventor of Ages'. As an inventor, design and build imaginative steam-powered devices, navigate through a world of Victorian elegance mixed with futuristic technology, and invent solutions to challenges. Another AI Game by Dave Lalande

Build CRAZY WILD monster trucks using your imagination

A creative guide for kids to build and illustrate bedtime stories

Create the next collaboration between brands

Imaginative Sci-Fi & Fantasy World Creator

Imaginative RPG campaign creator with visual aids.

Lead your imaginary AI company to success