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    Classifier-Free Guidance (CFG)

    AI fundamentals & modelsCFGClassifier-Free Diffusion GuidanceClassifier-free guidance
    Editorially reviewed on: 1 source
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    The short answer · 17 words

    In short

    Control method for diffusion models combining conditional and unconditional model predictions to enhance alignment with a default.

    Flow-alerta.cab (2026): Classifier-Free Guidance (CFG). https://flow-alerta.cab/en/glossar/classifier-free-guidance-cfg. Editorially reviewed on: 09 October 2026.

    How much detail would you like?

    Practical

    Basic concept of modern diffusion models and relevant for the understanding of prompt fidelity, diversity and sampling parameters.

    Example scenario: In text-to-image generation, the CFG value is adjusted to balance between greater prompt fidelity and more natural diversity.

    For proprietary generative workflows, test guidance values with representative prompts and jointly evaluate visual quality, prompt fidelity and consistency.

    At a glance

    Core
    Classifier-Free Guidance does not require a separate classifier. The diffusion model is trained in such a way that it can work both with and without condition; in the generation, both predictions are combined. A higher guidance score can increase conditional fidelity but deteriorate diversity and image quality.
    Use
    Basic concept of modern diffusion models and relevant for the understanding of prompt fidelity, diversity and sampling parameters.
    Example
    Example scenario: In text-to-image generation, the CFG value is adjusted to balance between greater prompt fidelity and more natural diversity.
    Limits
    Keep higher CFG value automatically for better images; confuse CFG with a separate classifier; compare values between different models directly.

    In context

    Questions about this term

    Does CFG need an external classifier?

    No.

    Is a higher value always better?

    No.

    Is CFG only used for images?

    The basic principle is more widely applicable to conditional diffusion models.

    All details and background

    Classifier-Free Guidance does not require a separate classifier. The diffusion model is trained in such a way that it can work both with and without condition; in the generation, both predictions are combined. A higher guidance score can increase conditional fidelity but deteriorate diversity and image quality.

    ELI5 – Explained simply

    To a certain extent, the model compares a variant with default and one without default and reinforces the direction that better fits the default.

    Why it matters in practice

    Basic concept of modern diffusion models and relevant for the understanding of prompt fidelity, diversity and sampling parameters.

    Example

    Example scenario: In text-to-image generation, the CFG value is adjusted to balance between greater prompt fidelity and more natural diversity.

    Pitfalls and limits

    • Keep higher CFG value automatically for better images; confuse CFG with a separate classifier; compare values between different models directly.

    Origin & history

    Ho and Salimans proposed Classifier-Free Diffusion Guidance 2021 as an alternative to classifier-based guidance; the work was widely published in 2022.

    Use in mid-sized companies

    • For proprietary generative workflows, test guidance values with representative prompts and jointly evaluate visual quality, prompt fidelity and consistency.

    Sources

    • arxiv.orgHo & Salimans, Classifier-Free Diffusion Guidance

    Quellenprüfung: 09.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.

    Editorially reviewed on:

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