Classifier guidance in diffusion models is a technique that helps improve the quality of generated samples by directing the diffusion process towards a particular category or class. In diffusion models, data is gradually transformed into noise and then back into a structured sample. Classifier guidance uses information from a pre-trained classifier during the denoising steps to encourage the generation of samples that belong to a desired class. Essentially, it influences the diffusion model to produce outputs that have specific characteristics defined by the classifier.
To implement classifier guidance, a classifier model is trained to distinguish between different classes based on the dataset the diffusion model is supposed to learn from. Once the classifier is trained, it can be employed during the inference phase where the diffusion model generates samples. By examining the intermediate noise samples and their predicted probabilities from the classifier, adjustments can be made to steer the noise samples in the direction of the desired class. The classifier’s output increases the likelihood that the final sample aligns with the expected characteristics of that class, resulting in higher-quality and more relevant outputs.
For example, imagine a diffusion model that generates images. If a developer wants the model to generate images of cats, they could use a classifier that has been trained to identify cats versus other animals. During the generation process, if the model produces an image that is not cat-like, the classifier’s feedback would guide the model's next steps to move closer to a cat image. This approach not only enhances the specific characteristics of the generated samples but also helps in fine-tuning the focus based on real-time feedback, resulting in more accurate and desirable outcomes.