Awesome GitHub RepositoriesDiffusion Sampling Methods

This is a framework for training and sampling diffusion models to generate high-fidelity images, video, and 4D assets. It provides a modular environment for managing generative AI training pipelines, including the handling of datasets, noise sampling, and loss weighting to stabilize the creation of synthetic content. The project features a modular model configuration system that uses YAML-based assembly to define network submodules and conditioners. It also includes a dedicated toolset for AI image watermarking, allowing for the embedding and detection of invisible markers to verify the origi

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

This project is a diffusion-based AI art generator and animation framework used to create digital images and motion graphics from text prompts. It functions as a system for producing stylized videos and AI art through iterative diffusion sampling and neural network models. The framework distinguishes itself through specialized tools for 3D depth animation, using depth-map transformations to create spatial movement. It also includes neural style transfer capabilities to apply specific artistic looks, such as watercolor or pixel art, and utilizes optical flow frame blending to reduce flickering

This is a classifier-guided diffusion framework for high-fidelity image generation. It implements a cascaded diffusion pipeline that chains a base diffusion model with a dedicated upsampler to progressively increase image resolution in stages, and uses classifier-guided diffusion sampling to steer the reverse diffusion process toward higher-quality outputs. The framework provides tools for training diffusion models from scratch using distributed processes with gradient accumulation, as well as training classifier models that provide gradient-based guidance during sampling. It supports both un

DiffSinger ist ein KI-Gesangssynthesizer und neuronaler Audiogenerator, der darauf ausgelegt ist, hochqualitativen Gesang und Sprache zu produzieren. Er fungiert als Text-to-Speech-System und als diffusionsbasiertes Tool zur Synthese von Gesangsstimmen, das Text und Tonhöhe in hörbares Audio transformiert. Das System nutzt einen flachen Diffusionsmechanismus und iterative Rauschverfeinerung, um realistische Gesangsdarbietungen zu generieren. Es integriert spezialisierte Sampling-Plugins und numerische Löser, um die Inferenz zu beschleunigen und die Zeit zu reduzieren, die zur Generierung synthetischer Stimmen erforderlich ist. Das Projekt deckt akustische Modellierung, Mel-Spektrogramm-Synthese und neuronale Vocoder-Rekonstruktion ab, um Text in Zeitbereichs-Audio-Wellenformen zu konvertieren. Es enthält zudem Funktionen zur synthetischen Stimmverbesserung, um die klangliche Qualität von Aufnahmen zu steigern.

GLIDE is a generative model designed for text-to-image synthesis, image editing, and the contextual filling of masked image regions. It uses a guided diffusion process to transform random noise into high-resolution imagery that aligns with descriptive text prompts. The system provides specialized capabilities for modifying existing visuals, including the ability to alter specific image elements and iteratively refine selected regions through text-driven guidance. It also functions as an inpainting tool, filling missing or masked sections of an image with new content that blends naturally with