According to an in-depth synthesis by researcher Lilian Weng, diffusion models have demonstrated outstanding capabilities in generating high-quality images, directly competing with Generative Adversarial Networks (GANs) that once dominated the field. This is a critical technical foundation that explains the boom of today's modern image generation models.
Background
Theoretically, diffusion models operate by defining a mathematical process to gradually add noise to data, then training a neural network to learn how to reverse this process to recover clean images from random noise. Lilian Weng notes that this architecture is extremely flexible in learning complex data distributions. Over time, the author has updated the analysis with a series of core advancements, such as latent diffusion models (LDM), classifier-free guidance, as well as performance optimization methods like progressive distillation and consistency models.
Why It Matters
For the engineering community and AI enthusiasts in Vietnam, this analysis provides a comprehensive overview of how to optimize the performance of image-generating AI systems. Instead of merely using closed APIs or off-the-shelf tools, understanding the diffusion mechanism allows developers to apply techniques like model distillation to reduce model size and accelerate inference speed, paving the way for deploying affordable image-generating AI on devices with limited hardware configurations.