Projects sharing features with StyleCLIP

Qwen-Image is a text-to-image model and large language model image generation framework. It functions as an AI image editing suite and a personalized image trainer, capable of producing high-fidelity visuals and accurate typography from natural language descriptions. The system is distinguished by its precision text rendering engine, which integrates multi-script calligraphy and layout-coherent alphabetic text into images. It provides specialized capabilities for subject identity preservation and consistent subject generation across different poses and viewpoints, alongside a training pipelin

qrbtf is an AI QR code generator and image synthesis system that blends machine-readable data with artistic imagery. It uses a latent diffusion model and spatial control networks to produce functional QR codes that incorporate visual art generated from descriptive text prompts. The system provides a dedicated interface and programmatic API for tuning visual output, allowing for the adjustment of control strength, padding ratios, and error correction levels. It supports deterministic sampling via random seeds and the use of negative prompts to refine the final aesthetic of the generated assets

Learning-Prompt is a collection of educational resources and step-by-step guides designed for mastering large language model interaction and text-to-image tools. It provides a guided course on prompt engineering for large language models alongside tutorials for creating visual content with generative AI. The project utilizes a curated curriculum that organizes material into sequential lessons and modular tracks. Instruction is delivered through step-by-step tutorials and an iterative framework of drafting, testing, and refining prompts, using side-by-side comparisons of raw and optimized exam

ml-mgie is a multimodal machine learning framework and image editor designed for instruction-based image manipulation. It utilizes multimodal large language models to translate natural language prompts into precise visual modifications, functioning as a text-to-image editing model. The system is a research implementation focused on aligning visual imagination with textual commands. It employs a training process based on image-pair datasets and descriptive instructions to learn how to execute complex visual edits. The framework covers capabilities in AI-powered visual content creation, includ

Instruct-pix2pix is an instruction-based image model and PyTorch library designed to modify visual content by following natural language directions. It functions as a diffusion model image editor that applies human-written instructions to existing pictures rather than using traditional text-to-image prompts. The project provides a fine-tunable diffusion framework for adapting pre-trained checkpoints to specific image editing datasets. It includes a synthetic dataset generator that creates paired images and text triplets to train models on various image editing tasks. The system covers a rang

Deep-daze is a neural image steerable generator and text-to-image synthesis tool. It functions as an image-to-image interpretation engine and an image generator that transforms text prompts and image seeds into visual representations. The system supports long-form text visualization by bypassing standard token limits to process extended narratives or poems. It also provides image-guided prompting, allowing the network to be initialized with a starting image before applying text steering. The framework employs neural network optimization and iterative gradient descent to refine image quality.

mmagic is a multimodal training pipeline and framework for generative AI, focusing on visual synthesis and restoration. It provides the infrastructure to build and train models for tasks such as text-to-image and text-to-video generation, 3D-aware content synthesis, and high-fidelity image translation using diffusion models and generative adversarial networks. The project distinguishes itself through specialized capabilities for generative model personalization, including techniques for fine-tuning subjects and styles. It also supports advanced visual manipulations such as latent space interp

Paper2gui is a multi-modal AI toolkit and model GUI wrapper designed to deploy and run various artificial intelligence models through a visual interface. Its primary purpose is to provide a way to execute complex AI research papers and models without requiring manual software installation or coding. The project distinguishes itself by using a wrapper-based model interface that abstracts command line arguments into visual input fields, utilizing template-driven UI generation to create parameter sliders and forms based on the specific requirements of the underlying model. It includes a centrali

DragGAN is an interactive generative image editor and manipulator that allows users to reshape visual output by moving handle points on a generative network manifold. It functions as a tool for point-based image editing, mapping user-defined coordinate shifts to a generative model's latent space to deform images. The system includes a generative image inversion tool that converts real photographs into latent representations. This process enables the interactive manipulation of non-generated content by bringing real-world images into a compatible format for the generative adversarial network.

imaginAIry is a system for generating and refining images and videos using diffusion models. It operates as a web-based server that triggers generation requests through standard API calls, allowing for the creation of visuals and video sequences from text prompts or existing files. The project provides a suite for AI image editing and upscaling, enabling the modification of visuals through natural language instructions and super-resolution tools to increase detail and image size. The system includes capabilities for structural image control using depth maps, edge maps, and body poses to main

IOPaint is an AI image editor and Stable Diffusion inpainting tool providing a web interface for removing objects and replacing image content. It utilizes latent diffusion image processing to synthesize high-resolution replacements for erased sections of an image. The project features a specialized AI background remover for isolating subjects and an AI image upscaler that employs super-resolution models for general photos and anime artwork. The software covers a broad range of capabilities including image segmentation for object isolation, face restoration for improving facial details, and t

ComfyUI-nunchaku is a 4-bit diffusion inference engine and a set of nodes for running low-precision quantized diffusion models within ComfyUI visual workflows. It provides a backend that reduces memory overhead and increases generation speed for transformer models. The project includes specialized tools for identity-preserving generation and an image-to-image guidance toolkit that uses depth maps and reference images. It also features a multimodal visual question answering implementation and a utility for merging multiple quantized model files into single unified files. The engine covers a b

This is a PyTorch-based implementation of diffusion models for synthesizing photorealistic images and video. It provides a framework for text-to-image and text-to-video generation, as well as unconditional image synthesis. The system utilizes a cascading diffusion pipeline to produce high-resolution imagery by passing low-resolution outputs through a sequence of super-resolution models. It also includes capabilities for image inpainting, allowing the reconstruction of masked or missing regions of visual media guided by surrounding context and text prompts. The project includes tools for diff

Sana is a framework for high-resolution image and video synthesis based on a linear diffusion transformer. It provides a toolkit for the training, fine-tuning, and execution of text-to-image and text-to-video models, as well as a video generative world model capable of simulating physical environments with precise spatial control. The project is distinguished by its use of linear complexity layers to handle high resolutions and its support for long-form, minute-length video generation in real time. It implements a two-stage inference paradigm that separates structural generation from visual t

IC-Light is a diffusion-based image editor and generative tool designed for controlling the illumination of foreground subjects. It functions as an image relighting system that uses latent diffusion models to modify lighting effects on isolated subjects. The project provides two primary methods for lighting control: text-based relighting, which uses descriptive prompts and lighting directions, and background-based relighting, which conditions the foreground lighting to match the visual properties of a provided background image. Beyond illumination, the system includes a surface normal estima

This repository is a collection of node-based pipeline configurations, examples, and templates for generating AI media. It provides a workflow library and a curated gallery of blueprints designed for creating images, videos, and 3D assets using diffusion models. The project specifically offers a set of pre-configured node graphs for implementing advanced image generation and refinement techniques, with a focus on Stable Diffusion workflows. These examples demonstrate how to interconnect processing nodes to define complex generative logic without writing code. The available templates cover a

Easy Diffusion is a desktop application that generates images from text descriptions using AI. It provides a straightforward interface for creating visuals by simply typing what you want to see, with the ability to preview images as they are being generated. The application supports loading custom AI models, allowing users to switch between different artistic capabilities and styles. It includes tools for editing existing images through text prompts or masks, applying predefined artistic styles like "Realistic" or "Pencil Sketch", and upscaling or correcting facial details after generation. F

dalle-mini is a text-to-image model and generative AI system designed to transform natural language descriptions into synthetic images. It functions as an image generation training toolkit and a generative model capable of creating visual representations from text prompts. The project provides a containerized deployment for consistent execution across different computing environments. It includes the necessary scripts and configuration files to train custom generative models from datasets. The system utilizes an autoregressive transformer architecture that treats visual data as discrete toke

Dream Textures is a Stable Diffusion integration for Blender that provides tools for text-to-image generation, depth projection, and node-based processing within a 3D environment. It functions as an AI texture generator capable of producing image textures and concept art from text prompts and scene renders. The system features a depth-to-image projection tool that maps generated imagery onto 3D models using depth data for spatial alignment. It also includes a node-based AI image processor for creating procedural visual effects and a dedicated toolset for AI-assisted inpainting and outpainting

Flux is a diffusion model inference engine designed for text-to-image generation and image-to-image manipulation. It provides a system for executing open-weight models to transform natural language descriptions into visual imagery or to modify existing images. The project distinguishes itself through a flow-matching framework for image generation and a structural image controller. This controller allows for guided synthesis by using depth maps and Canny edge detection to constrain the geometry and composition of the output. The toolkit covers a broad range of image editing capabilities, incl

This is a library of generative model architectures built using the TensorFlow framework. It provides implementations for producing synthetic data and realistic images, specifically focusing on Variational Autoencoders and various Generative Adversarial Network variants. The collection includes specific GAN architectures such as WGAN-GP, LSGAN, InfoGAN, and EBGAN. It also features Variational Autoencoders designed to learn latent representations and synthesize new samples from learned distributions. The project covers image processing pipelines for normalizing and cropping data, as well as a

Kolors is a generative model implementation for synthesizing photorealistic images from natural language descriptions and visual references. It utilizes a latent diffusion model framework to produce high-fidelity imagery, operating within a compressed latent space to improve generation efficiency and quality. The system functions as a multilingual image generator, interpreting text prompts in multiple languages to produce semantically accurate visual outputs. It includes a custom model training pipeline that uses low-rank adaptation to teach the model specific subjects or artistic styles from

This project is a framework for running Stable Diffusion image generation models on Apple Silicon using Core ML hardware acceleration. It provides a local generative AI pipeline for producing images from text prompts using Swift and Python without relying on external cloud APIs. The system includes a model converter to transform deep learning checkpoints into Core ML formats and a model optimizer to quantize weights and activations. It features a ControlNet integration layer to guide image generation using external signals such as edge and depth maps. Capabilities cover text-to-image generat

IF is a text-to-image diffusion system that translates natural language descriptions into visual imagery. The project provides a generative pipeline for creating images, an inpainting tool for modifying specific image sections, and a super-resolution upscaler to increase pixel density and clarity. The system includes a concept fine-tuning framework that allows for the teaching of new visual concepts by updating a small set of parameters. It also supports image style transfer to apply the aesthetic characteristics of a reference image to a new output.

This project provides a TensorFlow implementation of the Stable Diffusion model, serving as a generative engine for creating and modifying visual content. It functions as a machine learning architecture that translates natural language descriptions into high-quality images by iteratively refining noise within a compressed latent space. The system enables a variety of generative tasks, including text-to-image synthesis, image inpainting to fill missing or masked regions, and image editing to transform existing visuals based on text prompts. Beyond static imagery, the framework supports the gen

DiffusionBee is a Stable Diffusion desktop client for macOS that functions as an AI image generator and editor. It allows for the local generation of images from text prompts and the management of diffusion models without requiring external cloud services or technical setup. The application includes a local diffusion model manager for importing and switching between custom trained model files to achieve specific artistic styles. It also features a system for tracking generation history and uploading assets to a public gallery. The software covers several image synthesis and manipulation work

Stable Diffusion Web UI is a browser-based interface for generating, editing, and upscaling images and videos using latent diffusion models. It functions as a text-to-image generator, an AI image editor, and a tool for increasing image resolution and clarity. The system includes capabilities for custom model training, specifically allowing the creation of textual inversion embeddings to teach a model new concepts and visual styles from user photos. It also provides tools for AI video production, generating short clips from text prompts. The software covers image-to-image transformation, imag