Awesome GitHub RepositoriesUnconditional Generation

This is a PyTorch-based implementation of diffusion models for synthesizing photorealistic images and video. It provides a framework for text-to-image and text-to-video generation, as well as unconditional image synthesis. The system utilizes a cascading diffusion pipeline to produce high-resolution imagery by passing low-resolution outputs through a sequence of super-resolution models. It also includes capabilities for image inpainting, allowing the reconstruction of masked or missing regions of visual media guided by surrounding context and text prompts. The project includes tools for diff

mmagic is a multimodal training pipeline and framework for generative AI, focusing on visual synthesis and restoration. It provides the infrastructure to build and train models for tasks such as text-to-image and text-to-video generation, 3D-aware content synthesis, and high-fidelity image translation using diffusion models and generative adversarial networks. The project distinguishes itself through specialized capabilities for generative model personalization, including techniques for fine-tuning subjects and styles. It also supports advanced visual manipulations such as latent space interp

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

Este proyecto es un framework de deep learning para super-resolución de imágenes por IA y síntesis facial. Proporciona un escalador de imágenes de modelo de difusión y un sintetizador de imágenes faciales generativo capaz de transformar imágenes de baja resolución en salidas de alta resolución utilizando pesos de modelo preentrenados. El sistema utiliza refinamiento de difusión iterativo y muestreo guiado por baja resolución para restaurar detalles finos y nitidez. Admite tanto la generación de imágenes incondicional, donde las imágenes se crean desde cero, como la mejora de resolución guiada para la reconstrucción facial de alta fidelidad. El repositorio incluye un pipeline de entrenamiento de modelos de difusión con entrenamiento distribuido multi-GPU e inicialización de pesos preentrenados. Este entorno está respaldado por el seguimiento de experimentos de modelos, registro de métricas externas y reanudación de modelos basada en checkpoints.