Papers › Consistency Models Made Easy

Consistency Models Made Easy

20 Jun 2024arXiv:2406.14548archive 2025-07-28

Zhengyang Geng, Ashwini Pokle, William Luo, Justin Lin, J. Zico Kolter

Consistency models (CMs) offer faster sampling than traditional diffusion models, but their training is resource-intensive. For example, as of 2024, training a state-of-the-art CM on CIFAR-10 takes one week on 8 GPUs. In this work, we propose an effective scheme for training CMs that largely improves the efficiency of building such models. Specifically, by expressing CM trajectories via a particular differential equation, we argue that diffusion models can be viewed as a special case of CMs. We can thus fine-tune a consistency model starting from a pretrained diffusion model and progressively approximate the full consistency condition to stronger degrees over the training process. Our resulting method, which we term Easy Consistency Tuning (ECT), achieves vastly reduced training times while improving upon the quality of previous methods: for example, ECT achieves a 2-step FID of 2.73 on CIFAR10 within 1 hour on a single A100 GPU, matching Consistency Distillation trained for hundreds of GPU hours. Owing to this computational efficiency, we investigate the scaling laws of CMs under ECT, showing that they obey the classic power law scaling, hinting at their ability to improve efficiency and performance at larger scales. Our code (https://github.com/locuslab/ect) is publicly available, making CMs more accessible to the broader community.

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Code

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locuslab/ect officialmentioned in paperpytorch report

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1ran · honoured contract
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setup_snapshot_image_grid locuslab/ect/ct_eval.py official repository ran · our draft was wrong no licence file found · pointer only · a1023a0d09eca659 · report
generator_fn locuslab/ect/ct_eval.py official repository unverified no licence file found · pointer only · ccfabb29e5375ee5 · report
parse_int_list identical code first harvested elsewhere ran · honoured contract licence of this copy not recorded · cacd4f6ec202d9b4 · report

Tasks

Computational EfficiencyImage Generation

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation CIFAR-10 ECT FID 2.11 #16 of 78 Archive leaderboard report
Image Generation CIFAR-10 ECT NFE 2 #16 of 78 Archive leaderboard report
Image Generation ImageNet 64x64 ECM-XL FID 1.67 #15 of 65 Archive leaderboard report
Image Generation ImageNet 64x64 ECM-XL NFE 2 #15 of 65 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion

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