---
license: other
license_name: celeba-non-commercial-research
license_link: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
tags:
  - diffusion
  - ddpm
  - ddim
  - unconditional-image-generation
  - from-scratch
  - pytorch
  - mps
datasets:
  - flwrlabs/celeba
pipeline_tag: unconditional-image-generation
library_name: pytorch
---

# mini-diffusion — an 18.5M-parameter DDPM trained overnight on a laptop

A denoising diffusion model written from scratch in plain PyTorch. The U-Net, the noise schedule,
four samplers and the training loop are all in the repository — `diffusers` is never imported.

**Code: https://github.com/vous99/mini-diffusion**

Trained on one MacBook Pro (M5 Pro, 24 GB, MPS) in a single overnight run. Every number below was
measured on that run, not quoted from a paper.

## Results

| Metric | Value |
|---|---|
| **FID-tv** | **30.12** (10,000 samples, DDIM-50, EMA weights) |
| Steps | 22,098 |
| Images seen | 2.83M (17.4 epochs of CelebA train) |
| Best val loss | 0.0352 (`L_simple`, MSE on epsilon) |
| Training time | ~9 hours of compute at 87 images/s |
| Parameters | 18,538,371 |

FID over training: 73.35 (step 2,500) → 50.58 → 43.10 → 39.37 → 37.69 → 35.57 → 34.67 → 35.17
(step 20,000). Most of the gain lands in the first third of the run; the curve flattens after
roughly step 15,000.

**FID-tv is not the FID of the literature.** It uses torchvision's Inception-v3 weights rather than
the TensorFlow-ported weights every published FID is built on. The numbers here are internally
consistent — valid for comparing checkpoints, samplers and guidance scales — but not directly
comparable to a paper's "FID 3.5".

## What the model does

- Unconditional face generation at 64×64.
- Conditional generation on six CelebA attributes: `Male`, `Smiling`, `Young`, `Eyeglasses`,
  `Blond_Hair`, `Bangs`.
- Classifier-free guidance from the same weights — trained with 15% condition dropout against a
  learned null embedding, so one checkpoint serves both the conditional and unconditional branch.
- Deterministic DDIM with an `eta` parameter that reproduces ancestral DDPM exactly at `eta=1`.
- Karras sigma schedule with Heun's method.
- DDIM inversion, and therefore spherical interpolation between two real photographs.

## Usage

```bash
git clone https://github.com/vous99/mini-diffusion && cd mini-diffusion
pip install -r requirements.txt

python -c "
from huggingface_hub import hf_hub_download
import shutil, os
os.makedirs('ckpt', exist_ok=True)
shutil.copy(hf_hub_download('vous99/mini-diffusion', 'best.pt'), 'ckpt/best.pt')
"

python sample.py                                   # an 8x8 grid
python sample.py --attrs "Male=1,Eyeglasses=1"     # conditional
python sample.py --guidance-sweep                  # the guidance-scale figure
python sample.py --sampler-comparison              # DDPM / DDIM / Heun
```

Sampling needs no dataset. Retraining does — `prepare_data.py` rebuilds it from the HuggingFace
CelebA shards.

## Architecture

U-Net over four resolutions, `base_ch=64`, `channel_mults=(1,2,2,4)` → 64@64² 128@32² 128@16²
256@8². Two ResBlocks per level down, three up, self-attention at 16² and 8² and in the middle
block, `head_dim=32`, GroupNorm with 32 groups.

Conditioning is a `Linear(6, 256)` projection added to the sinusoidal timestep embedding, plus a
learned null vector. Stable Diffusion instead cross-attends to a *sequence* of text tokens; six
fixed flags are not a sequence, so addition is the honest analogue — the same mechanism, a simpler
carrier.

The output convolution is zero-initialised, so the loss at step 0 is exactly `E||eps||² = 1.0000`.
That single number confirms the target is epsilon, the data is scaled to [-1,1], and the reduction
is a mean.

## Training details

| | |
|---|---|
| Schedule | cosine (Nichol & Dhariwal), T=1000 |
| Prediction target | epsilon |
| Timestep sampling | stratified over the batch, not i.i.d. uniform |
| Optimizer | AdamW, lr 2e-4, betas (0.9, 0.999), weight decay 0, grad clip 1.0 |
| LR schedule | linear warmup 500 steps → cosine decay to 2e-5 |
| Batch | 64 × 2 gradient accumulation = 128 effective |
| EMA | 0.999 with warmup |
| Dropout | 0.0 |
| Augmentation | horizontal flip, p=0.5 |
| Precision | bfloat16 autocast (GroupNorm stays fp32) |

## Limitations

- **64×64 only.** Faces at higher resolution are out of scope for this budget.
- **No text conditioning.** The condition is six binary flags, not a prompt.
- **Aligned frontal portraits only.** CelebA is centred faces; profiles, multiple people and full
  bodies are out of distribution.
- **Roughly 15–20% of samples collapse into noise.** This is the dominant remaining defect.
- **Colour casts.** Even the cosine schedule ends at `abar_T ≈ 2.4e-9` rather than 0, so the model
  never trains on pure noise but is handed pure noise at sampling time. With epsilon-prediction
  this shows up as a per-image brightness and colour bias. It receded substantially over training
  but has not disappeared; v-prediction with zero terminal SNR is the proper fix.
- **This is 4.4% of the compute budget of the DDIM paper's CelebA-64 run.** The register is a good
  2016 GAN, not "indistinguishable from a photograph".

## Licence and intended use

The weights inherit the **CelebA licence: non-commercial research use only**. This is a study
artifact for understanding how diffusion models work, not a product component.

It generates synthetic faces of people who do not exist. Do not use it to impersonate real people
or to produce material presented as a genuine photograph of anyone.

## Checkpoint contents

`best.pt` is a `torch.save` dict with `model` (raw weights), `ema` (averaged weights — what you
should sample from), `cfg` (the `UNetConfig`), `diffusion` (the `DiffusionConfig`), `attributes`
(the six conditioning names, in order), `iter` and `val_loss`. Load it with `checkpoint.py` from
the GitHub repository.