Awesome GitHub RepositoriesExample-Based Image Generation

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

Neural Doodle is a collection of neural network tools designed for image upscaling, texture synthesis, and semantic-guided style transfer between visual inputs. It provides a semantic style transfer engine and an example-based image upscaler that increase image resolution by referencing visual details from a target style example. The project includes a neural texture synthesizer for creating seamless bitmap textures and repeating patterns from a single input style image. It also functions as an image generation tool capable of transforming simple sketches and photos into detailed artwork. Th

BasicSR is a PyTorch-based image restoration toolbox and framework designed for training and deploying deep learning models to upscale, denoise, and deblur images and videos. It serves as a comprehensive system for image super-resolution and video quality restoration, providing the necessary infrastructure to recover fine visual details and increase pixel density. The project distinguishes itself through specialized toolkits for facial image enhancement and high-fidelity face synthesis, as well as a dedicated video quality restoration suite that utilizes deformable convolutions and generative

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

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

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

Civitai is a platform for generative media creation and AI model distribution. It provides a centralized service for producing images, videos, audio, and music, while serving as a repository where users can share, discover, and browse custom model weights and fine-tuned adaptations. The platform distinguishes itself through a provider-agnostic orchestration layer that manages multi-step generation pipelines and complex workflows across different backends. It integrates with autonomous AI agents and editors via the Model Context Protocol, allowing external tools to access generation pipelines

StableCascade is a generative AI system and latent diffusion framework designed for text-to-image synthesis and image-to-image transformations. It utilizes a multi-stage cascade architecture that encodes and decodes images via a latent space to produce high-fidelity visual imagery. The system includes a cascade diffusion pipeline for controlling image structure through inpainting, outpainting, and super-resolution. It also provides a toolkit for image-to-image generation and the creation of image variations using embeddings. The framework supports model optimization through low-rank adaptati

ESRGAN is a deep learning image restoration framework designed for image super-resolution. It uses a generative adversarial network system to upscale low-resolution images into high-quality versions with sharp visual details and recovered fine textures. The framework implements a perceptual super-resolution model that optimizes the trade-off between perceived visual quality and pixel-level signal-to-noise ratio. It includes weight-interpolation blending to allow for the adjustment of visual sharpness and signal-to-noise ratios by mixing weights from different trained models. The system cover

A free and open-source inpainting & image-upscaling tool powered by webgpu and wasm on the browser。| 基于 Webgpu 技术和 wasm 技术的免费开源 inpainting & image-upscaling 工具, 纯浏览器端实现。

Clarity-upscaler es una herramienta de IA para el escalado y mejora de imágenes que utiliza modelos de aprendizaje profundo para aumentar la resolución y restaurar detalles visuales. Funciona como un motor de inferencia de superresolución que emplea redes neuronales para predecir píxeles faltantes y sintetizar detalles de alta frecuencia a partir de fuentes de baja resolución. El proyecto se entrega como una API programable, permitiendo la integración de procesamiento y enfoque automático de imágenes de alta resolución en aplicaciones y flujos de trabajo externos. Esta interfaz permite el escalado programático de imágenes para crear activos de alta resolución. El sistema proporciona capacidades para la mejora automática de imágenes, eliminando ruido y mejorando la claridad para producir versiones más nítidas de imágenes de baja calidad. Gestiona estas tareas mediante una arquitectura desacoplada de cliente-servidor que maneja la naturaleza intensiva en cómputo de la inferencia de aprendizaje automático.

Esta herramienta de superresolución de imágenes basada en PyTorch proporciona un pipeline de aprendizaje profundo para escalar imágenes de baja resolución. Utiliza redes generativas antagónicas (GAN) para aumentar la densidad de píxeles y reconstruir detalles de imágenes de alta resolución. El sistema incluye un escalador de imágenes basado en GAN y un pipeline de entrenamiento que optimiza los pesos de la red neuronal utilizando conjuntos de datos emparejados y funciones de pérdida personalizadas. Para gestionar los recursos de hardware, un procesador de imágenes basado en parches divide los archivos de alta resolución en segmentos más pequeños para evitar errores de asignación de memoria y bloqueos del sistema. Las capacidades adicionales incluyen la aplicación de pesos de modelos preentrenados para la eliminación de ruido y un monitor de entrenamiento de redes neuronales que visualiza métricas de rendimiento y archivos de registro a través de un panel de control.

Deep-daze es un generador de imágenes neuronal dirigible y una herramienta de síntesis de texto a imagen. Funciona como un motor de interpretación de imagen a imagen y un generador de imágenes que transforma prompts de texto y semillas de imagen en representaciones visuales. El sistema admite la visualización de texto de formato largo al omitir los límites estándar de tokens para procesar narrativas o poemas extensos. También proporciona prompts guiados por imagen, permitiendo que la red se inicialice con una imagen de partida antes de aplicar la dirección de texto. El framework emplea optimización de redes neuronales y descenso de gradiente iterativo para refinar la calidad de la imagen. Utiliza optimización de objetivos múltiples para equilibrar la dirección del texto y los objetivos basados en imágenes dentro de una única función de pérdida.