Generative AI

Artificial Intelligence 12 Sep 2026 6 min read

Control Diffusion Conditioning with Classifier-Free Guidance

A conditional diffusion model can follow its conditioning signal more strongly at sampling time without a separate classifier. Classifier-free guidance does this by evaluating a model in conditional and unconditional modes, then amplifying the difference between those predictions. That difference is the central mechanism. The guidance scale does not simply make a prompt louder in an abstract sense. It changes the denoising prediction along a direction defined by what the conditioning input contributes relative to an unconditional prediction at the same noisy state.

Artificial Intelligence Diffusion Models

Artificial Intelligence 08 Sep 2026 10 min read

Use Self-Conditioning in Diffusion Models

A diffusion model repeatedly turns a noisy state into a cleaner one. Each denoising call normally receives the current noisy sample, a noise level or timestep, and any external condition such as a text embedding. Yet the previous call has already produced useful information about what the clean sample may look like. Throwing that estimate away means the next call must reconstruct similar information again from the new noisy state.

Artificial Intelligence Diffusion Models

Artificial Intelligence 05 Sep 2026 9 min read

Classifier-Free Guidance in Diffusion Models

A conditional diffusion model may understand a prompt and still produce samples that only weakly reflect it. During generation, developers therefore often want a way to push the denoising trajectory toward the condition without training a separate classifier for every prompt or label. Classifier-free guidance (CFG) is a widely used way to do that. At each denoising step, the model is evaluated with the condition and without it. The difference between those predictions gives a direction associated with the condition, and a guidance scale controls how strongly sampling moves along that direction.

Artificial Intelligence Diffusion Models