AWhat changed
Used gradients from a separately trained classifier to steer diffusion sampling, with architecture ablations and upsampling integration to control image quality versus diversity.
Scientific scope
Used gradients from a separately trained classifier to steer diffusion sampling, with architecture ablations and upsampling integration to control image quality versus diversity. Diffusion and earlier conditional score ideas are inherited. Classifier guidance is distinct from classifier-free guidance; stronger guidance can reduce diversity and does not guarantee unbiased coverage or image correctness.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Diffusion and earlier conditional score ideas are inherited. Classifier guidance is distinct from classifier-free guidance; stronger guidance can reduce diversity and does not guarantee unbiased coverage or image correctness.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- Diffusion Models Beat GANs on Image Synthesis
- Dhariwal and Nichol: classifier-guided diffusion image synthesis — author/institutional project
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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