Classifier-Free Guidance

Classifier-Free Guidance linearly extrapolates between a conditional and an unconditional prediction so that sampling amplifies the condition $c$. During training the condition is dropped with probability $p$ (CFG dropout, usually 10–15%) and replaced by the empty token $\emptyset$, so one network learns both $\epsilon_\theta(x_t, c)$ and $\epsilon_\theta(x_t, \emptyset)$. At sampling the two noise predictions are combined with strength $w$:

$w=1$ reduces to plain conditional sampling; larger $w$ pushes the trajectory toward the conditional distribution (stronger prompt adherence) at the cost of diversity and oversaturated colors; typical values are $w = 7\text{–}8$ for Stable Diffusion and $w = 3\text{–}5$ for video/cascaded models. It is a score reweighting: it matches the behavior of classifier guidance $\nabla_{x_t}\log p(x_t) + \gamma\nabla_{x_t}\log p(c \mid x_t)$, pushing the score toward conditional likelihood, but needs no extra classifier.

prompt adherence in text-to-image/text-to-video (enabled by default in SD/Flux); a classic interview question — “how is CFG implemented, what w is typical, and why does large w oversaturate and reduce diversity?”.

plain conditional diffusion gives weak text control and off-prompt samples; classifier guidance needs a separately trained discriminator that is sensitive to adversarial noise; CFG needs one training run (dropout) and two forward passes (conditional + unconditional) at inference — no extra model — while substantially improving condition fidelity.

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