Papers › Diffusion Models Are Innate One-Step Generators
Diffusion Models Are Innate One-Step Generators
Bowen Zheng, Tianming Yang
Diffusion Models (DMs) have achieved great success in image generation and other fields. By fine sampling through the trajectory defined by the SDE/ODE solver based on a well-trained score model, DMs can generate remarkable high-quality results. However, this precise sampling often requires multiple steps and is computationally demanding. To address this problem, instance-based distillation methods have been proposed to distill a one-step generator from a DM by having a simpler student model mimic a more complex teacher model. Yet, our research reveals an inherent limitations in these methods: the teacher model, with more steps and more parameters, occupies different local minima compared to the student model, leading to suboptimal performance when the student model attempts to replicate the teacher. To avoid this problem, we introduce a novel distributional distillation method, which uses an exclusive distributional loss. This method exceeds state-of-the-art (SOTA) results while requiring significantly fewer training images. Additionally, we show that DMs' layers are differentially activated at different time steps, leading to an inherent capability to generate images in a single step. Freezing most of the convolutional layers in a DM during distributional distillation enables this innate capability and leads to further performance improvements. Our method achieves the SOTA results on CIFAR-10 (FID 1.54), AFHQv2 64x64 (FID 1.23), FFHQ 64x64 (FID 0.85) and ImageNet 64x64 (FID 1.16) with great efficiency. Most of those results are obtained with only 5 million training images within 6 hours on 8 A100 GPUs.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | CIFAR-10 | GDD-I | FID | 1.54 | #7 of 78 | Archive leaderboard | report |
| Image Generation | CIFAR-10 | GDD-I | NFE | 1 | #7 of 78 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | GDD-I | FID | 1.16 | #5 of 65 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | GDD-I | NFE | 1 | #5 of 65 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | GDD | FID | 1.42 | #9 of 65 | Archive leaderboard | report |
| Image Generation | ImageNet 64x64 | GDD | NFE | 1 | #9 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.
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