---
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).