Open Source Python AI Image Generators
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Python AI Image Generators
View 531 business solutionsBrowse free open source Python AI Image Generators and projects below. Use the toggles on the left to filter open source Python AI Image Generators by OS, license, language, programming language, and project status.
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ComfyUI
The most powerful and modular diffusion model GUI, api and backend
The most powerful and modular diffusion model is GUI and backend. This UI will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart-based interface. We are a team dedicated to iterating and improving ComfyUI, supporting the ComfyUI ecosystem with tools like node manager, node registry, cli, automated testing, and public documentation. Open source AI models will win in the long run against closed models and we are only at the beginning. Our core mission is to advance and democratize AI tooling. We believe that the future of AI tooling is open-source and community-driven. -
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Fooocus
Focus on prompting and generating
Fooocus is an open-source image generation software that simplifies the process of creating images from text prompts. Built on Gradio and leveraging Stable Diffusion XL, Fooocus eliminates the need for manual parameter tweaking, allowing users to focus solely on crafting prompts. It offers a user-friendly interface with minimal setup, making advanced image synthesis accessible to a broader audience. -
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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. The interface also supports prompt editing, batch processing, custom scripts, and many community extensions, making it a highly customizable and continually evolving platform for creative AI art generation. -
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FLUX.1
Official inference repo for FLUX.1 models
FLUX.1 repository contains inference code and tooling for the FLUX.1 text-to-image diffusion models, enabling developers and researchers to generate and edit images from natural-language prompts using open-weight versions of the model on their own hardware or within custom applications. The project is part of a larger family of FLUX models developed by Black Forest Labs, designed to produce high-quality, detailed visuals from text descriptions with competitive prompt adherence and artistic fidelity. This repo focuses on running the open-source model variants efficiently, providing scripts, model loading logic, and examples for local installations, and supports integration with Python toolchains like PyTorch and popular generative pipelines. Users can launch CLI tools to generate images, experiment with different FLUX variants, and extend the base code for research-oriented applications. -
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FLUX.2
Official inference repo for FLUX.2 models
FLUX.2 is a state-of-the-art open-weight image generation and editing model released by Black Forest Labs aimed at bridging the gap between research-grade capabilities and production-ready workflows. The model offers both text-to-image generation and powerful image editing, including editing of multiple reference images, with fidelity, consistency, and realism that push the limits of what open-source generative models have achieved. It supports high-resolution output (up to ~4 megapixels), which allows for photography-quality images, detailed product shots, infographics or UI mockups rather than just low-resolution drafts. FLUX.2 is built with a modern architecture (a flow-matching transformer + a revamped VAE + a strong vision-language encoder), enabling strong prompt adherence, correct rendering of text/typography in images, reliable lighting, layout, and physical realism, and consistent style/character/product identity across multiple generations or edits. -
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FastSD CPU
Fast stable diffusion on CPU and AI PC
FastSD CPU is an optimized fork of Stable Diffusion designed to run efficiently on CPUs and devices without dedicated GPUs by leveraging Latent Consistency Models and Adversarial Diffusion Distillation techniques that accelerate inference. It focuses on bringing fast text-to-image generation to mainstream hardware like desktop CPUs, lower-end laptops, or edge devices without requiring high-end graphics processors. The repository contains multiple interfaces including a desktop GUI for simple generation, an advanced web-based UI with support for extensions like LoRA and ControlNet, and a command-line interface for scripted usage or server deployments. With support for performance-oriented libraries such as OpenVINO and hardware acceleration on platforms like Intel AI PCs, FastSD CPU aims to shrink generation times dramatically compared with naive CPU implementations. -
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Stable Diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
Stable Diffusion Version 2. The Stable Diffusion project, developed by Stability AI, is a cutting-edge image synthesis model that utilizes latent diffusion techniques for high-resolution image generation. It offers an advanced method of generating images based on text input, making it highly flexible for various creative applications. The repository contains pretrained models, various checkpoints, and tools to facilitate image generation tasks, such as fine-tuning and modifying the models. Stability AI's approach to image synthesis has contributed to creating detailed, scalable images while maintaining efficiency. -
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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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InvokeAI
InvokeAI is a leading creative engine for Stable Diffusion models
InvokeAI is an implementation of Stable Diffusion, the open source text-to-image and image-to-image generator. It provides a streamlined process with various new features and options to aid the image generation process. It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM. InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. This fork is supported across Linux, Windows and Macintosh. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). We do not recommend the GTX 1650 or 1660 series video cards. They are unable to run in half-precision mode and do not have sufficient VRAM to render 512x512 images. -
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Dream Textures
Stable Diffusion built-in to Blender
Create textures, concept art, background assets, and more with a simple text prompt. Use the 'Seamless' option to create textures that tile perfectly with no visible seam. Texture entire scenes with 'Project Dream Texture' and depth to image. Re-style animations with the Cycles render pass. Run the models on your machine to iterate without slowdowns from a service. Create textures, concept art, and more with text prompts. Learn how to use the various configuration options to get exactly what you're looking for. Texture entire models and scenes with depth to image. Inpaint to fix up images and convert existing textures into seamless ones automatically. Outpaint to increase the size of an image by extending it in any direction. Perform style transfer and create novel animations with Stable Diffusion as a post processing step. Dream Textures has been tested with CUDA and Apple Silicon GPUs. Over 4GB of VRAM is recommended. -
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Qwen-Image
Qwen-Image is a powerful image generation foundation model
Qwen-Image is a powerful 20-billion parameter foundation model designed for advanced image generation and precise editing, with a particular strength in complex text rendering across diverse languages, especially Chinese. Built on the MMDiT architecture, it achieves remarkable fidelity in integrating text seamlessly into images while preserving typographic details and layout coherence. The model excels not only in text rendering but also in a wide range of artistic styles, including photorealistic, impressionist, anime, and minimalist aesthetics. Qwen-Image supports sophisticated editing tasks such as style transfer, object insertion and removal, detail enhancement, and even human pose manipulation, making it suitable for both professional and casual users. It also includes advanced image understanding capabilities like object detection, semantic segmentation, depth and edge estimation, and novel view synthesis. -
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HunyuanImage-3.0
A Powerful Native Multimodal Model for Image Generation
HunyuanImage-3.0 is a powerful, native multimodal text-to-image generation model released by Tencent’s Hunyuan team. It unifies multimodal understanding and generation in a single autoregressive framework, combining text and image modalities seamlessly rather than relying on separate image-only diffusion components. It uses a Mixture-of-Experts (MoE) architecture with many expert subnetworks to scale efficiently, deploying only a subset of experts per token, which allows large parameter counts without linear inference cost explosion. The model is intended to be competitive with closed-source image generation systems, aiming for high fidelity, prompt adherence, fine detail, and even “world knowledge” reasoning (i.e. leveraging context, semantics, or common sense in generation). The GitHub repo includes code, scripts, model loading instructions, inference utilities, prompt handling, and integration with standard ML tooling (e.g. Hugging Face / Transformers). -
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Diffusers
State-of-the-art diffusion models for image and audio generation
Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions. State-of-the-art diffusion pipelines that can be run in inference with just a few lines of code. Interchangeable noise schedulers for different diffusion speeds and output quality. Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. We recommend installing Diffusers in a virtual environment from PyPi or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation. -
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Ideogram 4
Open image model at the forefront of design
Ideogram 4 is an open-weight text-to-image model focused on high-quality visual generation, design control, and accurate text rendering inside images. It is built for users who need more than generic image generation, especially when layout, typography, composition, color, and language understanding matter. The project introduces a structured JSON prompting workflow that gives creators more explicit control over scene details and visual constraints. It can also accept plain-text prompts, making it accessible to users who prefer a simpler generation style. Ideogram 4 is especially useful for design-heavy outputs such as posters, ads, mockups, branded graphics, and images that include readable text. Its main value is combining open model access with professional-level control over image structure and visual direction. -
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Stable Diffusion WebUI Docker
Easy Docker setup for Stable Diffusion with user-friendly UI
Stable Diffusion WebUI Docker is a Docker-based repository that simplifies running Stable Diffusion with rich user interfaces by packaging multiple popular web UIs into an easy-to-deploy containerized solution. It integrates leading community UIs like AUTOMATIC1111 and ComfyUI into a Docker Compose setup that can be started with a single command, abstracting away dependency installation and environment configuration. Users can choose which UI profile they want to run — for example, full feature AUTOMATIC1111, CPU-only automatic builds, or ComfyUI workflows — and launch them in a consistent, isolated container environment with automatic model and data caching. The project supports mounting data and output directories so generated images and configurations persist outside the container, and it lets developers customize UI behavior through Docker Compose override files. -
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Free AI Watermark Remover - FreeRepair
AI-powered tool to quickly remove watermarks from images flawlessly
AI Watermark Remover (Free And Open-Source) & Make Blurry Images Clearer Or Larger Tool - FreeRepair, Simulation IOPaint Based On The Django Of Python With No Sign-Up. As a free, open-source, AI-powered tool, FreeRepair makes it easy to remove watermarks, logos, text or clutter from images, and blurry images can be made clearer or larger. No installation, no internet connection, it works out of the box, safe and secure, unlimited. -
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GLIDE (Text2Im)
GLIDE: a diffusion-based text-conditional image synthesis model
glide-text2im is an open source implementation of OpenAI’s GLIDE model, which generates photorealistic images from natural language text prompts. It demonstrates how diffusion-based generative models can be conditioned on text to produce highly detailed and coherent visual outputs. The repository provides both model code and pretrained checkpoints, making it possible for researchers and developers to experiment with text-to-image synthesis. GLIDE includes advanced techniques such as classifier-free guidance, which improves the quality and alignment of generated images with the input text. The project also offers sampling scripts and utilities for exploring how diffusion models can be applied to multimodal tasks. As one of the early diffusion-based text-to-image systems, glide-text2im laid important groundwork for later advances in generative AI research. -
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texturize
Generate photo-realistic textures based on source images
Generate photo-realistic textures based on source images. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture. A command-line tool and Python library to automatically generate new textures similar to a source image or photograph. It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. This software is powered by deep learning technology, using a combination of convolution networks and example-based optimization to synthesize images. We're building texturize as the highest-quality open source library available! The examples are available as notebooks, and you can run them directly in-browser thanks to Jupyter and Google Colab. -
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Big Sleep
A simple command line tool for text to image generation
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to wherever the command is invoked. If you have enough memory, you can also try using a bigger vision model released by OpenAI for improved generations. You can set the number of classes that you wish to restrict Big Sleep to use for the Big GAN with the --max-classes flag as follows (ex. 15 classes). This may lead to extra stability during training, at the cost of lost expressivity. -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and optionally the number of initial training steps. We can also feed in an image as an optimization goal, instead of only priming the generator network. Deepdaze will then render its own interpretation of that image. The regular mode for texts only allows 77 tokens. If you want to visualize a full story/paragraph/song/poem, set create_story to True. -
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Hunyuan3D-1
A Unified Framework for Text-to-3D and Image-to-3D Generation
Hunyuan3D-1 is an earlier version in the same 3D generation line (the unified framework for text-to-3D and image-to-3D tasks) by Tencent Hunyuan. It provides a framework combining shape generation and texture synthesis, enabling users to create 3D assets from images or text conditions. While less advanced than version 2.1, it laid the foundations for the later PBR, higher resolution, and open-source enhancements. (Note: less detailed public documentation was found for Hunyuan3D-1 compared to 2.1.). Community and ecosystem support (e.g. usage via Blender addon for geometry/texture). Integration into user-friendly tools/platforms. -
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
VQGAN-CLIP has been in vogue for generating art using deep learning. Searching the r/deepdream subreddit for VQGAN-CLIP yields quite a number of results. Basically, VQGAN can generate pretty high-fidelity images, while CLIP can produce relevant captions for images. Combined, VQGAN-CLIP can take prompts from human input, and iterate to generate images that fit the prompts. Thanks to the generosity of creators sharing notebooks on Google Colab, the VQGAN-CLIP technique has seen widespread circulation. However, for regular usage across multiple sessions, I prefer a local setup that can be started up rapidly. Thus, this simple Streamlit app for generating VQGAN-CLIP images on a local environment. Be advised that you need a beefy GPU with lots of VRAM to generate images large enough to be interesting. (Hello Quadro owners!). -
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ruDALL-E
Generate images from texts. In Russian
We present a family of generative models from SberDevices and Sber AI! Models allow you to create images that did not exist before. All you need is a text description in Russian or another language. Try to create unique images together with generative artists using your own formulations. Ask generative artists to depict something special for you as well. The Kandinsky 2.0 model uses the reverse diffusion method and creates colorful images on various topics in a matter of seconds by text query in Russian and other languages. You can even combine different languages within a single query. 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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Deface GUI - Face Anonymization Tool
Graphical User Interface Face Anonymization Tool
This application is a professional tool with a graphical user interface that enables anonymization of faces using the Deface Engine. Cross-Platform Compatible (Linux-Windows) NOTE: To use on Windows, first install Python. Then, if necessary, install “pip install deface” (only if necessary). -
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FLUX.1 Krea
Powerful open source image generation model
FLUX.1 Krea [dev] is an open-source 12-billion parameter image generation model developed collaboratively by Krea and Black Forest Labs, designed to deliver superior aesthetic control and high image quality. It is a rectified-flow model distilled from the original Krea 1, providing enhanced sampling efficiency through classifier-free guidance distillation. The model supports generation at resolutions between 1024 and 1280 pixels with recommended inference steps between 28 and 32 for optimal balance of speed and quality. FLUX.1 Krea is fully compatible with the FLUX.1 architecture, making it easy to integrate into existing workflows and pipelines. The repository offers easy-to-use inference scripts and a Jupyter Notebook example to facilitate quick experimentation and adoption. Users can run the model locally after downloading weights from Hugging Face and benefit from a live demo available on krea.ai.
