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Classifier-free guidance combines conditional and unconditional diffusion predictions at sampling time to trade off prompt adherence, sample quality, and diversity.
Déglul tegtal biiIA-nettali · ~1 min
The classifier-free guidance paper jointly trains conditional and unconditional predictions and combines their score estimates during sampling. Increasing guidance can change the quality-diversity tradeoff and may reduce diversity or cause artifacts. Standard implementation often evaluates the model for both conditional and unconditional inputs per denoising step. Exact behavior depends on the model’s parameterization and implementation.
Train with condition dropout to obtain conditional and unconditional behavior.
Adjust guidance scale in a controlled comparison.
Measure both prompt adherence and output diversity.
Account for the additional model evaluation used by standard guidance.
Guidance remains an inference-time control with tradeoffs. Tune and evaluate it for the specific model and task rather than assuming larger values always improve results.
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Classifier-free guidance combines conditional and unconditional diffusion predictions at sampling time to trade off prompt adherence, sample quality, and diversity.
Njàngale bu amul klassifikatër dafay gëna yokk topp gi ci lu gaaw ci extrapoler ci lu amul sart dem ci wax luy am sart.
Bu ñuy tàggat, yenn saa yi dañuy bàyyi laaj bi, suko defee benn reso jàng jikko bu am sart ak bu amul sart.
Njàngale mu gëna kawe dafay forse adhésion bu gëna dëgër ak man-man yu gëna am doole, waaye puus ko fu sori lool dafay jur nataal yu bari, yu metti, yu bari ay artfact.
Modèle bi dañu koy doxal benn yoon ak prompt bi ak benn yoon amul, ginaaw ga ñu boole prediction yi, lu tollu ci ñaari pass ci jéego bu nekk.
Suñu jëfandikoo benn reso ngir ñaari xam-xam yi, dina moytu fragile, classifier bu gëna mag bi diffusion guidée bu yàgg bi soxla.
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Ci bi mu melGis bi ci topp
OCR-Free Document Understanding (Donut)
IA buy wane