File size: 2,421 Bytes
b494181 542c2a7 2937b63 542c2a7 2937b63 542c2a7 2937b63 542c2a7 2937b63 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | ---
license: mit
---
# Diffusion Memorization — Model Checkpoints
Trained VAE and score-model checkpoints for the paper accompanying
[trevorbchen/diffusion_memorization](https://github.com/trevorbchen/diffusion_memorization)
(code) and
[trevorbchen/diffusion_memroization](https://huggingface.co/datasets/trevorbchen/diffusion_memroization)
(curated results / diverse-1k subset indices).
> **Status: private.** Accompanies a paper currently under review. Please do
> not share this link or make it public until the authors say otherwise.
**Scope:** these are the checkpoints behind the paper's headline MLP-based
results. The spatial-DiT pilot experiment and its checkpoints are
intentionally excluded from this release.
## Contents
```
vae_checkpoints/
├── celeba_resnet_modernloss_d{10,15,20,...,200}/vae.pt # final CelebA VAE, 14 widths
└── cifar10_resnet_beta005_d{10,20,...,260}/vae.pt # final CIFAR-10 VAE, 19 widths
results/
├── celeba_diverse1k_bigmlp_sgd_lr001_m08_10k_5m/
│ └── seed{42..N}/d{10,...,200}/
│ ├── last_model.pt # MLP score-network checkpoint (hidden=1024, depth=5)
│ ├── config.json # exact training hyperparameters
│ ├── metrics.jsonl # per-eval-step memorization_fraction_pixel, FID, loss
│ └── runner.log # training log (includes any NaN/retry history)
└── cifar10_beta005_diverse1k_bigmlp_sgd_lr001_m08_10k_5m/
└── seed{42..N}/d{20,...,260}/ (same structure)
```
VAEs: ResNet conv architecture, beta-VAE with free bits and capacity warmup
(`--arch resnet`, see the code repo's `standard_vae.py` /
`train_vae_celeba_standard_tar.py` for exact args). Score models: SGD
(lr=0.001, momentum=0.80), trained against the diverse-1k subset in the
dataset repo, 5,000,000 steps, memorization/FID evaluated every 100k steps
against 10k generated samples in pixel space.
Memorization ratio: `d(gen, NN1_train) / d(gen, NN2_train)`, memorized if
`< 1/3` (Bonnaire/Somepalli). See `lib/metrics.py` in the code repo.
Some `d260` CIFAR-10 runs went non-finite under this fixed LR/momentum at
high latent width — see `runner.log` for affected seeds; the paper's clean
tables use replacement seeds for those cells rather than a different
optimizer configuration (kept consistent across all `d` for a fair
comparison).
## License
MIT (matching the code repository).
|