Journey from Prompt to Visual Fest: Unlocking Stable Diffusion #innovation

Unlocking the Power of Stable Diffusion: A Journey from Prompt to Visual Fest | by MUJ ACM | Jul, 2024

The content discusses the mathematics behind stable diffusion in machine learning models. The objective function is described as a contrastive function that adjusts model weights to increase similarity scores for correct image-caption pairs. During training, a large number of image-text pairs are processed in batches, with only a small portion being accurate. The objective function aims to maximize similarity scores for correct pairs while minimizing others.

Diffusion models undergo forward and reverse processes iteratively by adding and subtracting noise. The forward diffusion process is represented mathematically using a normal distribution. Linear scheduling is used to bound the variance, ensuring consistent values. The reverse diffusion process is similarly represented mathematically with fixed variance values.

The loss function of these processes is calculated as -log(p,q), representing a negative likelihood. Variational lower bound techniques are applied to simplify the formulae, leading to the prediction of noise in the model. Ultimately, the noise is calculated as the square of the difference between two noise variables. The content provides a detailed explanation of the mathematical processes involved in stable diffusion in machine learning models.

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