Diffusion Models

Generative models that learn to reverse a gradual noise-adding process.

A diffusion model is trained by progressively adding noise to data and learning how to reverse that corruption. During generation, it begins with random noise and repeatedly refines it into a structured output.

Diffusion models power many modern image, video, audio and 3D-generation systems. They offer high-quality outputs and strong controllability, although generation can require many computational steps.

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Small Language Models Retrieval-Augmented Generation Prompt Engineering Context Engineering Few-Shot Learning Supervised Fine-Tuning

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