Enhancing Diffusion Models for High-Quality Image Generation

TL;DR

This paper enhances diffusion models for image generation by integrating advanced techniques like classifier-free guidance and latent diffusion, improving speed and quality on datasets like CIFAR-10 and ImageNet-100.

Contribution

It introduces optimized methods for diffusion models, including CFG and VAE integration, to boost efficiency and image quality in generative AI.

Findings

DDIM + CFG achieves faster inference and higher image quality

VAE and noise scheduling present challenges needing further research

Models perform well on CIFAR-10 and ImageNet-100 datasets

Abstract

This report presents the comprehensive implementation, evaluation, and optimization of Denoising Diffusion Probabilistic Models (DDPMs) and Denoising Diffusion Implicit Models (DDIMs), which are state-of-the-art generative models. During inference, these models take random noise as input and iteratively generate high-quality images as output. The study focuses on enhancing their generative capabilities by incorporating advanced techniques such as Classifier-Free Guidance (CFG), Latent Diffusion Models with Variational Autoencoders (VAE), and alternative noise scheduling strategies. The motivation behind this work is the growing demand for efficient and scalable generative AI models that can produce realistic images across diverse datasets, addressing challenges in applications such as art creation, image synthesis, and data augmentation. Evaluations were conducted on datasets including CIFAR-10 and ImageNet-100, with a focus on improving inference speed, computational efficiency, and image quality metrics like Frechet Inception Distance (FID). Results demonstrate that DDIM + CFG achieves faster inference and superior image quality. Challenges with VAE and noise scheduling are also highlighted, suggesting opportunities for future optimization. This work lays the groundwork for developing scalable, efficient, and high-quality generative AI systems to benefit industries ranging from entertainment to robotics.