# GAN

AuraSR-v2 is a GAN-based image super-resolution model designed for enlarging generated images, and it is a variant of the GigaGAN paper. The PyTorch implementation of this model is based on the unofficial lucidrains/gigagan-pytorch repository. It significantly enhances the resolution of images while preserving their quality, which is particularly important for applications requiring high-definition image outputs.

AuraSR is a Super-Resolution model based on GAN, which enhances the quality of generated images through image conditional enhancement techniques. The model is implemented as a variant of the GigaGAN paper and utilizes the Torch framework. AuraSR's strength lies in its ability to effectively improve the resolution and quality of images, making it suitable for the field of image processing.

HairFastGAN is a method for high-resolution, near real-time performance, and excellent reconstruction hair transfer. It employs a novel architecture operating within the FS latent space of StyleGAN, enhanced recovery methods, and an improved encoder for better alignment, color transfer, and post-processing. In the most challenging cases, it can transfer hair shape and color from one image to another in under a second.

Creative Upscaler is an AI-based image generator that allows anyone to quickly and easily create high-quality artworks. It integrates a variety of cutting-edge machine learning algorithms such as Stable Diffusion, DALL-E 2, VQGAN+CLIP, and supports generating images in various styles. Users simply provide a text description, and Creative Upscaler can automatically generate the images. Additionally, it features a Creative Image Aerator, which can convert low-resolution images into high-definition images. Creative Upscaler is completely free to use, boasts a large and active community, and is the best choice for exploring AI art.

AnimateLCM is a deep learning-based model for generating animation videos. It is capable of producing high-fidelity animation videos with minimal sampling steps. Unlike direct consistency learning from the original video dataset, AnimateLCM adopts a decoupled consistency learning strategy, decouples the extraction of image generation priors and motion generation priors, thereby enhancing training efficiency and the visual quality of the generated animations. Furthermore, AnimateLCM can be integrated with plugins from the Stable Diffusion community to achieve various controllable generation features. AnimateLCM has demonstrated its performance in image-based video generation and layout-based video generation.

PanoHead is a 360° geometric sensory 3D full head synthesis method that can be trained solely using unstructured outdoor images to achieve consistent 360° full-body head image synthesis with various appearances and detailed geometric shapes.

GigaGAN is a large-scale GAN model designed for text-to-image synthesis. It boasts fast inference speeds, the ability to generate high-resolution images, and an editable latent space, enabling various potential space editing applications such as latent interpolation, style mixing, and vector arithmetic operations. GigaGAN can generate 512-pixel images at a rate of up to 7.7 images per second and supports 16-megapixel image synthesis. It is an efficient text-to-image synthesis model applicable to diverse scenarios.

Inst-Inpaint is an image restoration algorithm that can estimate and remove objects to be deleted based on natural language input. This product offers the GQA-Inpaint dataset and a novel restoration framework called Inst-Inpaint, capable of removing objects from images based on textual prompts. The product provides various GAN and diffusion baselines and conducts experiments on both synthetic and real-world image datasets. It utilizes different evaluation metrics to measure the quality and accuracy of the models, demonstrating significant quantitative and qualitative improvements.