vae-vs-ddpm-celeba

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

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Files in this repository

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.

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Results (v1.2)

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.

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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.

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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.