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 是一款 AI 图像放大与增强工具，利用深度学习模型提高图像分辨率并恢复视觉细节。它作为一个超分辨率推理引擎，使用神经网络预测缺失像素，并从低分辨率源中合成高频细节。 该项目以可编程 API 的形式提供，允许将自动高分辨率图像处理和锐化功能集成到外部应用程序和工作流中。该接口支持通过编程方式放大图像，以创建高分辨率资产。 该系统提供自动图像增强功能，通过去除噪点和提高清晰度，生成低质量图像的更锐利版本。它通过解耦的客户端-服务器架构来处理机器学习推理中计算密集型的任务。

该基于 PyTorch 的图像超分辨率工具提供了一个用于放大低分辨率图像的深度学习流水线。它利用生成对抗网络（GAN）来增加像素密度并重建高分辨率图像细节。 该系统包括一个基于 GAN 的图像放大器和一个使用配对数据集和自定义损失函数优化神经网络权重的训练流水线。为了管理硬件资源，基于补丁（patch-based）的图像处理器将高分辨率文件分割成较小的片段，以防止内存分配错误和系统崩溃。 其他功能包括应用预训练模型权重进行降噪，以及一个通过仪表板可视化性能指标和日志文件的神经网络训练监视器。

Deep-daze 是一个神经图像可控生成器和文本转图像合成工具。它作为一个图像到图像的解释引擎和图像生成器，将文本提示和图像种子转换为视觉表现。 该系统支持长文本可视化，通过绕过标准令牌限制来处理扩展的叙述或诗歌。它还提供图像引导提示，允许网络在应用文本控制之前使用起始图像进行初始化。 该框架采用神经网络优化和迭代梯度下降来提高图像质量。它使用多目标优化在单个损失函数内平衡文本控制和基于图像的目标。