alexjc/neural-doodleArchived

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

This project is a neural network extension for Stable Diffusion that provides spatial control and geometric consistency for text-to-image generation. It functions as an image structure controller and conditioning tool, enabling the use of external inputs to guide the layout and geometry of generated imagery. The framework is distinguished by its ability to transform input images into structural guides through various preprocessors. These include the extraction of depth maps, normal maps, and human pose landmarks, as well as the detection of Canny edges, anime lineart, and straight architectur

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

DeOldify is a deep learning system and a set of pre-trained computer vision models designed to apply realistic colors to grayscale photographs and video footage. It functions as a neural media restoration tool that uses trained networks to estimate original hues for black-and-white media and remove glitches and artifacts from aged images and film. The project employs a NoGAN colorization technique that removes the GAN discriminator during training to prevent artifacts and avoid over-saturation of pixels. For cinematic sequences, it applies temporal frame consistency to maintain color stabilit