#GAN

GFPGAN offers a versatile tool for real-world blind face restoration by leveraging pretrained face GAN models, such as StyleGAN2, to deliver natural-looking improvements even on low-resolution images. Latest updates provide models like V1.3 and V1.4 for finer results, and compatibility with platforms such as Huggingface Spaces. The algorithm also supports background enhancement via Real-ESRGAN and runs on various operating systems, ideal for projects requiring comprehensive face and image restoration.

The HAT project showcases a novel method for image restoration with emphasis on super-resolution. Utilizing advanced pixel activation, it enhances image quality on datasets like Set5, Set14, and Urban100, independent of ImageNet pretraining. The project includes GAN-based models tailored for sharper and more accurate results. Discover comprehensive performance insights through the available codes and pre-trained models, alongside straightforward testing and training guidance for practical application in real-world scenarios of image super-resolution.

Discover how Consistency Decoder enhances decoding for Stable Diffusion VAEs. This project implements advanced Consistency Models for reliable image creation, ensuring consistent image outputs through superior decoding methods. Designed to maintain high image fidelity, the decoder provides stable results across diverse applications. Easy to install and use, it seamlessly integrates with existing Stable Diffusion Pipelines and supports high-performance hardware like CUDA-enabled GPUs. Explore practical examples showcasing improved image clarity over conventional GAN decoders, representing a substantial advancement in image generation technology.

Discover the method for creating text-to-video models using GANs in Python. This guide covers key processes such as data coding, pre-processing, and GAN implementation for efficient video generation, suitable for those with limited computing resources.

Access a PyTorch library for solving SDEs with efficient GPU support and backpropagation. Supports diverse applications such as variational autoencoders and GANs, while being compatible with Python 3.8 and PyTorch 1.6.0. Provides documentation and examples for both latent and neural SDEs, ensuring easy integration into machine learning models. Developed by Google Research, it offers scalable computations without overstating its capabilities.

The project provides a complete PyTorch implementation of StyleGAN2, allowing training of generative adversarial networks directly via command line. It features easy setup with multi-GPU support and data-efficient training techniques for generating high-quality synthetic images, including cities and celebrity faces. Additionally, it includes options for model customization and improvements like attention mechanisms and top-k training for enhanced GAN performance. Suitable for developers interested in a straightforward yet effective tool for AI-generated imagery.

Explore a GAN-based super-resolution tool for precise real-world image upscaling. By utilizing techniques from GigaGAN and implemented in Torch, this tool reduces seam artifacts and is perfect for photography, art, and data visualization. Simple Python integration allows for up to 4x image enhancement.

Utilize a specialized PyTorch model trainer featuring auto-optimization, mixed precision training, and gradient accumulation. Includes options for DDP and Accelerate, and a batch size finder to optimize resource usage. Offers customization through callback support and integration with loggers such as Tensorboard and ClearML. Evaluate performance with profiling tools and engage in community development with anonymized telemetry. Ideal for developers requiring sophisticated model training capabilities.

This open-source project offers a PyTorch implementation designed to enhance neural vocoders with GAN-based methods through the Slicing Adversarial Network. Key sound quality metrics like M-STFT, PESQ, and MCD are improved. Built on BigVGAN, it supports efficient model training using the LibriTTS dataset. Comprehensive instructions and pretrained model checkpoints are available, making it a valuable resource for researchers and developers focused on advancing audio synthesis and speech processing.