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
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 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.
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:
git clone https://github.com/AhmedAbdAlkreem/vae-vs-ddpm-celeba
cd vae-vs-ddpm-celeba
pip install -r requirements.txt
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 directly — they handle config loading, device placement, and output saving for you.
Training data
CelebFaces Attributes (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 in the source repo.