classifierfree

Classifierfree, in the context of diffusion models, refers to a technique commonly called classifier-free guidance. It enables conditional image or data generation without the need for a separate, trainable classifier to steer the output. Instead, the model is trained to generate under both conditioned inputs (such as a text prompt) and unconditioned inputs, typically by using a null or empty conditioning during part of training.

During sampling, the model produces two denoised predictions for each step: one conditioned on the prompt and one unconditional. These predictions are combined with a guidance weight to emphasize the conditional signal while preserving diversity. The effect is that the generated content tends to adhere more closely to the prompt without requiring an external classifier to judge alignment. This method leverages the learned joint distribution of data and conditioning within the diffusion model itself.

Classifier-free guidance has become popular in text-to-image diffusion models and related generative systems. It is associated with improving prompt fidelity, reducing reliance on additional classifiers, and enabling more controllable generation. Implementations are commonly used in models designed for open-ended image synthesis, such as widely deployed open and commercial diffusion-based tools.

Limitations include increased computational cost due to dual predictions per denoising step and the need to tune the guidance scale carefully. If set too high, the system can overemphasize the prompt and introduce artifacts or degrade sample diversity. Despite these caveats, classifier-free guidance remains a central technique for steering diffusion models without external classifiers.