Advancing Image Synthesis: Methods and Applications of Latent Diffusion Models
Soumika Chakraborty · International Journal for Research in Applied Science and Engineering Technology · 2024
Diffusion models (DMs) have revolutionized the field of generative modelling, delivering exceptional results in tasks such as image synthesis, inpainting, and super-resolution. Despite their success, the reliance on pixel-space processing in these models has imposed substantial computational challenges, requiring hundreds of GPU days for training and significant resources for inference. In this work, we introduce Latent Diffusion Models (LDMs), an innovative framework that addresses these limitations by operating within a perceptually compressed latent space derived from a pretrained autoencoder. This paradigm shift significantly reduces the computational complexity of both training and inference while maintaining the high fidelity and diversity of the generated outputs. LDMs leverage a two-stage approach: a pretrained autoencoder for efficient latent-space representation and a diffusion model trained directly within this space. By introducing cross-attention layers into the architecture, LDMs also support flexible conditioning on various modalities such as text descriptions, semantic maps, or lowresolution images. This versatility enables the model to perform a range of tasks, including text-to-image generation, classconditional image synthesis, and high-resolution super-resolution. Our experiments demonstrate that LDMs achieve competitive or state-of-the-art performance across multiple benchmarks, including CelebA-HQ, ImageNet, and MS-COCO, while requiring significantly fewer computational resources than pixel-space diffusion models. For instance, LDMs reduce training time by up to 2.7× and inference memory requirements by 50%, all while improving sample quality. This work highlights the potential of latent-space generative models to democratize access to advanced generative AI technologies, making them feasible for researchers and practitioners with limited computational resources. At the same time, we discuss the ethical considerations of using such models, including their potential misuse for creating manipulated content. Latent Diffusion Models pave the way for efficient, scalable, and high-quality image synthesis, providing a robust foundation for future advancements in generative modelling.