Research map / Image generation / Fast diffusion sampling
Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add Self-Conditioning at sampling and training (ablation) | Bit Diffusion / DDPM, DPM-Solver 20 NFE (x0 prediction) · ImageNet 64x64 continuous | FID: 12.13 -> 6.94 | Helped |
| add Self-Conditioning (eps prediction) (ablation) | continuous diffusion · ImageNet 64x64 continuous, DPM-Solver 20 NFE | FID: 6.10 -> 4.24 | Helped |
| add Self-Conditioning on binary encodings (ablation) | Bit Diffusion · CIFAR-10 | FID | Helped |
| add asymmetric time intervals (time difference 0 -> 7/8) at sampling (ablation) | Bit Diffusion captioning, 10 steps · MS-COCO captioning | BLEU-4: 17.6 -> 34.6 | Helped |
| add asymmetric time intervals (ablation) | Bit Diffusion captioning, 5 steps · MS-COCO captioning | BLEU-4: 17.1 -> 31.6 | Helped |
| add non-zero time difference at 400 steps (ablation) | Bit Diffusion · ImageNet 64x64 | FID | No clear effect |
| add non-zero time difference at 100 steps (ablation) | Bit Diffusion · ImageNet 64x64 | FID | Helped |
| use uint8 (rand) categorical encoding instead of uint8 (ablation) | Bit Diffusion · ImageNet 64x64 class-conditional | FID: 4.84 -> 8.76 | Hurt |
| replace base-2 uint8 with one-hot encoding (ablation) | Bit Diffusion l2 loss · CIFAR-10 unconditional | FID: 3.48 -> 46.32 | Hurt |
| replace l2 loss with logistic loss (ablation) | Bit Diffusion uint8 (rand) · CIFAR-10 unconditional | FID: 6.93 -> 49.29 | Hurt |
| replace l2 loss with logistic loss (ablation) | Bit Diffusion uint8 / gray code · CIFAR-10 unconditional | FID: 3.48 -> 3.53; 3.86 -> 3.71 | No clear effect |
| use self-guidance sampling instead of default Self-Conditioning sampling (ablation) | Bit Diffusion · ImageNet 64x64 class-conditional | FID: uint8 (rand) 8.76 -> 7.87; uint8 4.84 -> 5.15 | Mixed |
Closest papers by meaning
- BitsFusion: 1.99 bits Weight Quantization of Diffusion Model (NeurIPS 2024 · similarity 0.90)
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- Compressed Image Generation with Denoising Diffusion Codebook Models (ICML 2025 · similarity 0.88)
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