Projects sharing features with IcGAN

pix2pix is a framework for image-to-image translation using conditional generative adversarial networks. It functions as a supervised trainer and visual domain mapper designed to learn a mapping between input and output images for style and domain transfer. The system utilizes a U-Net encoder-decoder architecture combined with a PatchGAN local discriminator to enforce high-frequency local consistency. It employs L1 loss regularization to ensure generated outputs remain structurally close to the ground truth. The project covers a broad range of computer vision capabilities, including semantic

Image Deblurring using Generative Adversarial Networks

Pytorch implementation

iGAN is a framework for producing synthetic images using generative adversarial networks. It provides a web-based interface for interactively creating and editing imagery across categories such as landscapes, architecture, and fashion using pre-trained models. The system enables precise control over visual output through latent space exploration, interpolation, and projection. Users can guide the generative process using an interactive editor featuring sketching, coloring, and warping brushes to refine specific regions or shapes in real-time. The project supports both automated scripted gene

pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into high-resolution photorealistic images. It functions as a high-resolution image synthesizer and an image-to-image translation model capable of producing synthetic images at 2048x1024 resolution. The system includes a semantic image editor that allows for the modification of high-resolution visuals by updating the underlying semantic label maps. This enables interactive image editing and the generation of photorealistic images based on source images or discrete label maps. The framework pro

This project is a generative adversarial network implementation and research framework. It provides the tools and hyperparameters necessary to train and evaluate generative models across various datasets, specifically designed to reproduce results from academic research. The framework includes a Parzen density likelihood estimator to calculate model log likelihood. This allows for the quantitative evaluation of generative distributions and the measurement of overall model performance. The codebase covers machine learning research capabilities, focusing on the training of adversarial networks

MagicQuill is a suite of interactive tools for image segmentation, diffusion-based editing, layered composition, and prompt-guided visual synthesis. It functions as a diffusion model image editor and a layered visual composition tool, enabling the addition, removal, and recoloring of image elements through a combination of sketches and text prompts. The system features a prompt-guided image generator that predicts editing instructions by analyzing user drawings to automatically populate text prompts. It allows for visual style control by swapping generative model weights to shift outputs betw

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

This project is a deep learning style transfer framework designed to apply artistic styles to photographs. It functions as a photorealistic image stylizer that merges the content of one image with the visual characteristics of another while maintaining the original geometry and structural details. The system distinguishes itself through the use of matting Laplacian matrices and semantic segmentation masks to prevent distortion and preserve edge fidelity. These capabilities allow for region-specific styling, where different aesthetics can be applied to distinct objects or areas within a single

DeepFaceLab is a deep learning software suite designed for face swapping and the creation of deepfake videos. It functions as a neural network image compositor that replaces human faces or entire heads in video files to produce synthetic media. The tool provides capabilities for digital facial manipulation, including the ability to modify the perceived age of people in video sequences. It uses automated pattern recognition to blend source faces onto target frames to create seamless visual composites. The system covers a broad technical surface including landmark-based face alignment, autoenc

DeepDream is a deep learning image processor and convolutional neural network art generator designed to synthesize psychedelic imagery and visualize how neural networks interpret visual data. It functions as a tool for generating generative AI art by amplifying patterns recognized by a pre-trained model to produce dream-like effects. The project utilizes a TensorFlow image visualizer to explore how different layers of a neural network perceive images. This is achieved through algorithmic image manipulation and deep learning visualization techniques that transform standard photographs into sty

Primitive is an algorithmic art generator and geometric image reconstruction tool that transforms raster images into stylized vector compositions. It functions as an iterative shape optimizer and raster-to-vector converter, approximating pixel-based photos by layering geometric primitives such as triangles, circles, and rectangles. The project utilizes a search algorithm to determine the optimal position, size, and color for each shape to minimize the visual difference from the source image. Users can apply shape constraint definitions to control the properties and orientations of the geometr

This project is a Stable Diffusion WebUI extension that provides a graphical interface for personalized portrait generation and AI photo editing. It allows users to train custom identity models from a small set of uploaded images to create consistent digital versions of specific people. The extension includes a virtual try-on system that replaces clothing in images by aligning reference garments with template bodies. It also features tools for face swapping in both static images and videos, as well as a portrait animator that transforms static images into dynamic videos using reference-guided

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

clmtrackr is a JavaScript computer vision library designed for facial landmark detection and real-time tracking. It implements Constrained Local Models to identify specific coordinate points on a human face within video feeds or static images. The project functions as a real-time face warping engine and expression analysis tool. It can distort facial images via parametric models to create caricatures or identify and label emotional states such as happiness, sadness, anger, and surprise based on feature coordinates. The library covers a broad range of capabilities including automatic and manu

PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

Tensorflow code for the Bayesian GAN (https://arxiv.org/abs/1705.09558) (NIPS 2017)

The author's officially unofficial PyTorch BigGAN implementation.

This repository contains the source code for the CVPR oral paper Animating Arbitrary Objects via Deep Motion Transfer by Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci and Nicu Sebe. We call the proposed deep framework Monkey-Net, as it enables motion transfer by…

Tensorflow implementation for reproducing main results in the paper Self-Attention Generative Adversarial Networks by Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena.

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

This is a TensorFlow implementation of the Deep Convolutional Generative Adversarial Network (DCGAN) architecture, providing a framework for training generative models that produce synthetic images from random noise vectors. The project implements the core DCGAN design, using transposed convolutions for upsampling, batch normalization for training stability, and leaky ReLU activations in the discriminator, all executed as static TensorFlow computation graphs. The implementation supports training on custom image datasets by accepting user-supplied image folders without requiring a predefined 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

This project is a generative adversarial network designed for image animation and motion transfer. It functions as a computer vision framework that synthesizes video sequences by applying motion patterns extracted from a driving video onto a static source image. The model distinguishes itself by using a keypoint-based representation to decouple object appearance from temporal movement. By tracking structural deformations through learned latent coordinates, it performs motion retargeting and synthetic media production without requiring manual annotations or object-specific training data. The