Methods › Computer Vision › Image Generation Models › GFCG
Gradient-Free Classifier Guidance
GFCG
Introduced by Rahul Shenoy et al. in Gradient-Free Classifier Guidance for Diffusion Model Sampling
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Classifier guided sampling from diffusion models involves the computation of gradients of classifier probabilities, and this is computationally expensive as it involves the use of autograd operators. To mitigate this issue, Classifier-Free Guidance (CFG), was proposed to use an unconditional sample as a reference to increase contrast from. We extend these concepts to devise a novel formulation that utilizes a classifier, without computation of gradients, to generate an conditional sample as the reference. In what follows, we describe this methodology and refer to our method as “gradient-free classifier guidance” (GFCG). Our method is also adaptive in that it computes the guidance scale on the-fly depending on how confused the diffusion model is during the denoising process.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Gradient-Free Classifier Guidance for Diffusion Model Sampling 23 Nov 2024 · 0 repositories · arXiv:2411.15393
Tasks archive 2025-07-28
3 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Generation | 1 |
| Text to Image Generation | 1 |
| Text-to-Image Generation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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