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arxiv 2204.00227 v1 pith:S2LKD7AX submitted 2022-04-01 cs.CV cs.LG

Perception Prioritized Training of Diffusion Models

Jooyoung Choi , Jungbeom Lee , Chaehun Shin , Sungwon Kim , Hyunwoo Kim , Sungroh Yoon This is my paper
classification cs.CV cs.LG
keywords levelsdiffusionmodelsnoisecorrupteddatalearnloss
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i.e., denoising score matching loss. In this paper, we show that restoring data corrupted with certain noise levels offers a proper pretext task for the model to learn rich visual concepts. We propose to prioritize such noise levels over other levels during training, by redesigning the weighting scheme of the objective function. We show that our simple redesign of the weighting scheme significantly improves the performance of diffusion models regardless of the datasets, architectures, and sampling strategies.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Variance Reduction for Expectations with Diffusion Teachers

    cs.LG 2026-05 unverdicted novelty 6.0

    CARV amortizes upstream diffusion teacher costs over noise resamples with timestep importance sampling and stratified-inverse-CDF sampling, delivering 2-3x effective compute gains in text-to-3D experiments and order-o...

  2. Variance Reduction for Expectations with Diffusion Teachers

    cs.LG 2026-05 unverdicted novelty 5.0

    CARV introduces a hierarchical Monte Carlo estimator with amortized reuse, importance sampling, and stratification that yields 2-3x effective compute gains on diffusion-teacher pipelines while cutting gradient varianc...

  3. TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation

    cs.CV 2024-09 unverdicted novelty 4.0

    TextBoost is a one-shot personalization technique that selectively fine-tunes the text encoder of diffusion models using causality-preserving adaptation and lightweight adapters to reduce parameters and storage.