---
license: mit
tags:
  - diffusion
  - ddpm
  - cifar-10
  - image-generation
  - pytorch
datasets:
  - cifar10
metrics:
  - mse
pipeline_tag: unconditional-image-generation
---

# DDPM CIFAR-10 Diffusion Model

A Denoising Diffusion Probabilistic Model (DDPM) trained on CIFAR-10 for 300 epochs. This model generates 32×32 synthetic images across 10 classes (airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck).

## Model Architecture

| Component            | Specification                    |
| -------------------- | -------------------------------- |
| Architecture         | U-Net with self-attention        |
| Parameters           | **26.8 M**                       |
| Base channels        | 128                              |
| Channel multipliers   | [1, 2, 2, 2]                     |
| Attention resolutions | 16×16, 8×8, 4×4 (multi-head=4)  |
| ResBlocks per stage  | 2                                |
| Dropout              | 0.1                              |
| Normalization        | GroupNorm (32 groups)            |
| Activation           | SiLU                             |
| Time embedding       | Sinusoidal → MLP(128→512→512)    |

### U-Net Data Flow

```
Input (3×32×32)
  → Init Conv (3→128)
  → Down[0]: ResBlocks(128) → 32×32 skip
  → Down[1]: ResBlocks(256) + SelfAttn → 16×16 skip
  → Down[2]: ResBlocks(256) + SelfAttn → 8×8 skip
  → Down[3]: ResBlocks(256) + SelfAttn → 4×4 skip
  → Mid: ResBlock + SelfAttn + ResBlock
  → Up[0]: SkipCat + ResBlocks(256) + SelfAttn → upsample 8×8
  → Up[1]: SkipCat + ResBlocks(256) + SelfAttn → upsample 16×16
  → Up[2]: SkipCat + ResBlocks(256) + SelfAttn → upsample 32×32
  → Up[3]: SkipCat + ResBlocks(128)
  → Out: GroupNorm + SiLU + Conv → 3×32×32
```

## Diffusion Process

- **Forward diffusion**: Linear noise schedule, predicting noise ε (ε-prediction) rather than x₀
- **Schedule**: Cosine β schedule from 1e-4 to 0.02 over T=1000 timesteps
- **x₀ clipping**: Predicted x₀ is clipped to [-1, 1] before computing posterior mean (prevents numerical explosion)
- **Sampling**: DDPM ancestral sampler with EMA shadow weights
- **Objective**: Simple MSE loss between predicted and true noise

## Training

| Setting             | Value              |
| ------------------- | ------------------ |
| Dataset             | CIFAR-10 (50k)     |
| Epochs              | 300                |
| Batch size          | 256                |
| Optimizer           | AdamW              |
| Learning rate       | 2×10⁻⁴             |
| Mixed precision     | BF16 (AMP)         |
| EMA decay           | 0.9999 (warmup)    |
| Steps               | 58,500             |
| Final loss           | ~0.0547            |
| Hardware            | RTX 5080 16 GB     |

## Usage

```python
import torch
import json
from safetensors.torch import load_file

from config import Config
from model import UNet
from diffusion import GaussianDiffusion

# Load config and model
with open('config.json') as f:
    cfg_dict = json.load(f)

cfg = Config()
cfg.model.base_channels = cfg_dict['base_channels']
cfg.model.channel_multipliers = tuple(cfg_dict['channel_multipliers'])
cfg.model.attention_resolutions = tuple(cfg_dict['attention_resolutions'])
# ... (set remaining fields from config.json)

model = UNet(cfg.model)
state_dict = load_file('model.safetensors')
model.load_state_dict(state_dict)
model.eval().cuda()

# Set up diffusion
diff = GaussianDiffusion(
    timesteps=1000,
    beta_start=1e-4,
    beta_end=0.02,
)

# Generate 64 images (8×8 grid)
with torch.no_grad():
    samples = diff.p_sample_loop(model, (64, 3, 32, 32), device='cuda')
    # samples.shape → (64, 3, 32, 32), range [-1, 1]
```

## Samples

Training progression — same 8 random seeds tracked across the run:

| Step 500 | Step 30,000 | Step 58,500 (final) |
|----------|-------------|---------------------|
| Early blurry shapes | Semi-recognizable objects | Sharp, diverse CIFAR-10 samples |

## Limitations

- **Resolution**: Fixed 32×32 — CIFAR-10 native resolution
- **Class conditioning**: This is an unconditional model; no class labels used during training
- **FID**: Not evaluated (training-in-progress checkpoint)
- **Artifacts**: Some generated samples may have checkerboard artifacts or unnatural colors

## Citation

```bibtex
@inproceedings{ho2020denoising,
  title={Denoising Diffusion Probabilistic Models},
  author={Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
  booktitle={Advances in Neural Information Processing Systems},
  year={2020}
}
```