A First-order Generative Bilevel Optimization Framework for Diffusion Models

• URL: http://arxiv.org/abs/2502.08808v1
• Date: Wed, 12 Feb 2025 21:44:06 GMT
• Title: A First-order Generative Bilevel Optimization Framework for Diffusion Models
• Authors: Quan Xiao, Hui Yuan, A F M Saif, Gaowen Liu, Ramana Kompella, Mengdi Wang, Tianyi Chen,
• Abstract summary: Diffusion models iteratively denoise data samples to synthesize high-quality outputs. Traditional bilevel methods fail due to infinite-dimensional probability space and prohibitive sampling costs. We formalize this challenge as a generative bilevel optimization problem. Our first-order bilevel framework overcomes the incompatibility of conventional bilevel methods with diffusion processes.
• Score: 57.40597004445473
• License:
• Abstract: Diffusion models, which iteratively denoise data samples to synthesize high-quality outputs, have achieved empirical success across domains. However, optimizing these models for downstream tasks often involves nested bilevel structures, such as tuning hyperparameters for fine-tuning tasks or noise schedules in training dynamics, where traditional bilevel methods fail due to the infinite-dimensional probability space and prohibitive sampling costs. We formalize this challenge as a generative bilevel optimization problem and address two key scenarios: (1) fine-tuning pre-trained models via an inference-only lower-level solver paired with a sample-efficient gradient estimator for the upper level, and (2) training diffusion models from scratch with noise schedule optimization by reparameterizing the lower-level problem and designing a computationally tractable gradient estimator. Our first-order bilevel framework overcomes the incompatibility of conventional bilevel methods with diffusion processes, offering theoretical grounding and computational practicality. Experiments demonstrate that our method outperforms existing fine-tuning and hyperparameter search baselines.

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