Classifier-free guidance (Ho & Salimans, 2022) works by computing two denoising predictions per step: one conditional (using your prompt) and one unconditional (ignoring the prompt). The final prediction amplifies the difference: output = unconditional + scale × (conditional − unconditional). Scale=1 means no guidance (just the conditional prediction). Scale=7 means the model amplifies the prompt's influence 7x beyond what it would naturally do.
Higher guidance makes the image more "prompt-aligned" but at a cost: the model overshoots, producing oversaturated colors, unrealistic lighting, and visual artifacts. Very high guidance (15+) often produces images that look like they've been run through a sharpening filter — technically matching the prompt but aesthetically poor. The sweet spot depends on the model: SD 1.5 works well at 7–9, SDXL at 5–8, and Flux at 3–5.
进阶 techniques manipulate guidance during generation: starting with high guidance (to establish composition) and reducing it in later steps (to refine details naturally). Negative CFG (guidance scale below 1) inverts the prompt's effect, generating the opposite of what's described — useful for understanding what the model associates with specific concepts but rarely useful for actual image generation.