Papers › One-Step Diffusion Distillation via Deep Equilibrium Models

One-Step Diffusion Distillation via Deep Equilibrium Models

12 Dec 2023NeurIPS 2023 11arXiv:2401.08639archive 2025-07-28

Zhengyang Geng, Ashwini Pokle, J. Zico Kolter

Diffusion models excel at producing high-quality samples but naively require hundreds of iterations, prompting multiple attempts to distill the generation process into a faster network. However, many existing approaches suffer from a variety of challenges: the process for distillation training can be complex, often requiring multiple training stages, and the resulting models perform poorly when utilized in single-step generative applications. In this paper, we introduce a simple yet effective means of distilling diffusion models directly from initial noise to the resulting image. Of particular importance to our approach is to leverage a new Deep Equilibrium (DEQ) model as the distilled architecture: the Generative Equilibrium Transformer (GET). Our method enables fully offline training with just noise/image pairs from the diffusion model while achieving superior performance compared to existing one-step methods on comparable training budgets. We demonstrate that the DEQ architecture is crucial to this capability, as GET matches a 5× larger ViT in terms of FID scores while striking a critical balance of computational cost and image quality. Code, checkpoints, and datasets are available.

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get_1d_sincos_pos_embed_from_grid locuslab/get/models/get.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · e5947aba1d10885f · report
get_2d_sincos_pos_embed locuslab/get/models/get.py official repository ran · honoured contract MIT (permissive) · c92c27c924b517e8 · report
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sample_fid locuslab/get/utils.py official repository ran MIT (permissive) · 2afb8c77febf8d22 · report
create_logger locuslab/get/utils.py official repository unverified MIT (permissive) · 3a3f19b2829e0116 · report
get_2d_sincos_pos_embed_from_grid locuslab/get/models/get.py official repository unverified MIT (permissive) · 665d8a4e8f673a4c · report

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Methods

Absolute Position EncodingsAdamAttentionBPEDEQDense ConnectionsDiffusionDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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