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Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Distill the EDM teacher into a single-step generator | EDM · CIFAR-10 unconditional | IS: 9.05 -> 9.89 | Helped |
| Distill the EDM teacher into a single-step generator | EDM · CIFAR-10 unconditional | FID: 3.55 -> 4.53 | Mixed |
| Distill the EDM teacher into a single-step generator | EDM · ImageNet 64×64 class-conditional | FID: 6.20 -> 5.57 | Helped |
| Apply Diff-Instruct to the pretrained GAN generator | StyleGAN2+ADA+Tune · CIFAR-10 conditional | FID: 2.42 -> 2.27 | Helped |
| Apply Diff-Instruct to the pretrained GAN generator | StyleGAN2+ADA+Tune · CIFAR-10 conditional | IS: 10.14 ± 0.09 -> 10.11 ± 0.10 | Hurt |
| Apply Diff-Instruct to the pretrained GAN generator | StyleGAN2+ADA+Tune · CIFAR-10 unconditional | FID: 2.92 -> 2.71 | Helped |
| Apply Diff-Instruct to the pretrained GAN generator | StyleGAN2+ADA+Tune · CIFAR-10 unconditional | IS: 9.83 ± 0.04 -> 9.86 ± 0.04 | Helped |
Closest papers by meaning
- Revisiting Diffusion Models: From Generative Pre-training to One-Step Generation (ICML 2025 · similarity 0.93)
- Unlocking Dataset Distillation with Diffusion Models (NeurIPS 2025 · similarity 0.90)
- DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture (CVPR 2025 · similarity 0.89)
- DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents (ICML 2024 · similarity 0.89)
- Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding (ICML 2024 · similarity 0.89)
- Why DiT Models Underperform as Representation Learners without Long Skip Connections (NeurIPS 2026 · similarity 0.88)
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