Diffúzióalapú

Diffúzióalapú, meaning diffusion-based in Hungarian, refers to a class of generative artificial intelligence models that have gained significant traction in recent years. These models operate by gradually adding noise to data, such as images, and then learning to reverse this process, effectively generating new, coherent data from random noise. The core idea is to model the data distribution as a Markov process where noise is incrementally introduced over a series of steps, and the generative process involves a corresponding denoising procedure.

The process typically involves a forward diffusion process, where data is corrupted with Gaussian noise until it becomes indistinguishable from pure noise. The model is then trained to predict the noise that was added at each step. During inference, the model starts with random noise and iteratively applies the learned denoising function to gradually transform the noise into a sample that resembles the training data distribution. This step-by-step refinement allows for the generation of high-quality and diverse outputs.

Diffúzióalapú models have demonstrated remarkable success in various domains, most notably in image generation. They are capable of producing photorealistic images, creating artistic styles, and performing image editing tasks. Their ability to capture complex data distributions and generate novel samples has made them a powerful tool in fields like computer vision, natural language processing, and even scientific research. The underlying mathematical framework, often rooted in stochastic differential equations, provides a robust foundation for their generative capabilities.