hojonathanho/diffusion

Features

• Diffusion Model Training - Implements a complete training pipeline for generative diffusion models to learn image data distributions.
• Latent Space Encoders - Utilizes compressed representations of images to reduce computational overhead during diffusion.
• Generative Model Training Tools - Provides pipelines to train generative models on image datasets using a denoising process.
• U-Net Architectures - Employs a U-Net convolutional encoder-decoder architecture with skip connections to predict and subtract noise.
• Denoising Schedulers - Provides linear and cosine variance schedules to control noise addition at each training step.
• Image Generation and Synthesis - Ships a full workflow for training and testing models that generate high-quality synthetic images.
• Iterative Noise Removal - Implements an iterative process to transform random noise into structured images by removing Gaussian noise.
• Generative Model Evaluation - Provides automated frameworks to ensure generated samples meet specific quality and performance standards.
• Model Evaluation Tools - Supplies a set of automated tools to evaluate the performance and quality of diffusion model outputs.
• Model Performance Evaluators - Includes specialized scripts to assess the quality and accuracy of generated image samples.
• Stochastic Gradient Descent - Optimizes model weights using stochastic gradient descent based on mean squared error of predicted noise.
• Diffusion Foundations - Foundational denoising diffusion probabilistic models.