Diffusion Models at Scale: Techniques, Applications, and Challenges

Diffusion models have emerged as a powerful class of generative models, offering state-of-the-art performance across various domains such as image synthesis, audio generation, and molecular design. Their unique approach, which involves modeling data distributions through iterative noise addition and denoising processes, has established them as a robust alternative to traditional generative frameworks like GANs and VAEs. However, the scalability of diffusion models—essential for handling high-dimensional data, large-scale datasets, and complex multimodal tasks—poses significant challenges. This survey provides a comprehensive overview of scalable diffusion models, focusing on the innovations that enable their efficient training and sampling. We explore advancements in noise schedules, neural architectures, and sampling acceleration techniques, alongside strategies for training on large-scale datasets and deploying models in resource-constrained environments. Furthermore, we highlight the transformative applications of scalable diffusion models across fields such as creative content generation, healthcare, scientific research, and more. Despite their successes, diffusion models face critical challenges, including computational inefficiency, resource-intensive training, and ethical concerns related to bias and misuse. We discuss these open challenges and outline promising directions for future research, emphasizing the need for interdisciplinary collaboration and task-specific adaptations. By addressing these challenges, scalable diffusion models have the potential to redefine the boundaries of generative modeling, driving innovation and enabling new applications in science, technology, and creative industries. This survey aims to serve as a valuable resource for researchers and practitioners seeking to understand and advance the field of diffusion models.