---
license: mit
library_name: pytorch
tags:
  - pytorch
  - vae
  - variational-autoencoder
  - ddpm
  - diffusion
  - diffusion-models
  - image-generation
  - celeba
  - from-scratch
  - computer-vision
datasets:
  - jessicali9530/celeba-dataset
pipeline_tag: unconditional-image-generation
---

# VAE vs DDPM — CelebA (from scratch)

Pretrained checkpoints for a from-scratch PyTorch **Variational Autoencoder (VAE)** and **Denoising Diffusion Probabilistic Model (DDPM)**, both trained on 64×64 CelebA face crops as a head-to-head comparison of the two generative model families.

Both models are implemented from base `torch.nn` primitives only — no pretrained VAE, no `diffusers` library.

📎 **Full training code, configs, and technical write-up:** [AhmedAbdAlkreem/vae-vs-ddpm-celeba](https://github.com/AhmedAbdAlkreem/vae-vs-ddpm-celeba)

---

## Files in this repository

| File | Size | Description |
|---|---|---|
| `vae_best.pt` | 34.7 MB | Best VAE checkpoint (v1.2) |
| `ddpm_best.pt` | 107 MB | Best DDPM checkpoint (v1.2) — U-Net with residual blocks + self-attention (8×8, 16×16) + sinusoidal time embedding |

Both checkpoints correspond to **v1.2** in the source repo's version history — the current best result after a documented sampler bug fix (v1.0 → v1.1) and architectural refinements (v1.1 → v1.2). See the [CHANGELOG](https://github.com/AhmedAbdAlkreem/vae-vs-ddpm-celeba/blob/main/CHANGELOG.md) for the full iteration history.

---

## Results (v1.2)

| Model | FID ↓ | Inception Score ↑ | Reconstruction PSNR ↑ | Sampling cost |
|---|---|---|---|---|
| VAE | 97.06 | 2.00 ± 0.06 | 22.47 dB | 1 forward pass |
| DDPM | **26.42** | 2.61 ± 0.17 | n/a | 1000 forward passes (full DDPM) |

DDPM produces sharper, more photorealistic faces at a much higher sampling cost; the VAE is near-instant to sample but characteristically blurry — an architectural trait of pixel-wise reconstruction loss, not a training deficiency. Full quantitative and qualitative comparisons (sample grids, latent-space PCA, interpolations, denoising trajectory) are in the [GitHub README](https://github.com/AhmedAbdAlkreem/vae-vs-ddpm-celeba#quantitative-comparison).

---

## How to use these checkpoints

The model architectures are custom (defined in the source repo under `src/models/`), so these `.pt` files are raw `state_dict` weights, not a `transformers`/`diffusers`-compatible model. To load them:

```bash
git clone https://github.com/AhmedAbdAlkreem/vae-vs-ddpm-celeba
cd vae-vs-ddpm-celeba
pip install -r requirements.txt
```

```python
import torch
from huggingface_hub import hf_hub_download
from src.models.vae import VAE
from src.models.unet import UNet  # DDPM denoiser

# Download checkpoints from this Hub repo
vae_ckpt = hf_hub_download("UseItOrLoseIt/vae-vs-ddpm-celeba", "vae_best.pt")
ddpm_ckpt = hf_hub_download("UseItOrLoseIt/vae-vs-ddpm-celeba", "ddpm_best.pt")

vae = VAE()
vae.load_state_dict(torch.load(vae_ckpt, map_location="cpu"))
vae.eval()

unet = UNet()
unet.load_state_dict(torch.load(ddpm_ckpt, map_location="cpu"))
unet.eval()
```

For ready-made sampling and inference scripts (VAE reconstruction, DDPM full/DDIM sampling, latent interpolation), use `sample.py` and `inference.py` from the [source repo](https://github.com/AhmedAbdAlkreem/vae-vs-ddpm-celeba) directly — they handle config loading, device placement, and output saving for you.

---

## Training data

[CelebFaces Attributes (CelebA) Dataset](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset), aligned/cropped to 64×64. v1.2 was trained on a 40,000-image subset (VAE: 40 epochs, DDPM: 50 epochs).

## Limitations

- The VAE has not changed since v1.0 — a perceptual (VGG) loss is implemented in the source config but not yet enabled; this is the most likely lever for reducing VAE blur further.
- DDIM (fast, ~50-step) sampling is implemented but not yet benchmarked against the full 1000-step DDPM sampler.
- Training budget (40k images, tens of epochs) is modest relative to published DDPM results on full CelebA (~200k images, hundreds of epochs) — current FID/IS numbers should be read as credible for this budget, not as a ceiling on the architecture.

## License

MIT — see [LICENSE](https://github.com/AhmedAbdAlkreem/vae-vs-ddpm-celeba/blob/main/LICENSE) in the source repo.