Compact Diffusion Models are designed to offer a more efficient alternative to traditional diffusion models. These models aim to be smaller and faster, while maintaining effectiveness, making them suitable for a variety of applications where computational resources or time are limited.
- Efficiency: Smaller model size and faster processing without significant loss in quality.
- Flexibility: Suitable for a range of applications where traditional diffusion models are typically used.
configs: Configuration files for the models.ddpm.py: Training script for unconditional DDPM generation.ddpm_conditional.py: Training script for conditional DDPM generation.generate.py: Script for generating outputs using the models.modules.py: Contains various modules used in the models.noising_test.py: Script for testing the noising process.pruning.py: Implements pruning techniques for model efficiency.quantize.py: Quantization methods for model optimization.run.sh: Shell script for running the model.single_generation.py: Script for single image generation.utils.py: Utility functions used across the project.
To use these models, clone this repository and install the required dependencies. Example usage can be found in the run.sh script.
- Python 3.x
- Dependencies as listed in
environment.ymlfile. - Recreate the original environment using
conda env create -f environment.yml.
| Setup | Architecture | Method | EMA | MPT | Size (MB) | Inference Time | FID |
|---|---|---|---|---|---|---|---|
| 1 | UNet | Base | × | × | 93.4 | 27s | 29.72 |
| 2 | UNet | Base | ✓ | × | 93.4 | 22s | 25.84 |
| 3 | UNet | Base | ✓ | ✓ | 93.4 | 22s | 25.92 |
| 4 | UNet | + Pruning | ✓ | ✓ | 64.2 | 21s | 43.36 |
| 5 | UNetMini | Compression | ✓ | ✓ | 23.7 | 18s | 33.82 |
| 6 | UNetMini | + KD loss | ✓ | ✓ | 23.7 | 18s | 37.82 |
| 7 | UNetMini | + KD loss + Pruning | ✓ | ✓ | 15.3 | 19s | 59.01 |
| Model Setup | Generated Images |
|---|---|
| 1. Base |  |
| 2. Base + EMA |  |
| 3. Base + MPT |  |
| 4. Base + KD + Pruning |  |
| 5. Mini + EMA + MPT |  |
| 6. Base + KD |  |
| 7. Base + Pruning |  |
This project is licensed under the Apache-2.0 License - see the LICENSE file for details.
- Our base code is built on top of @dome272 work. We build several new optimizations, and methods on top of this codebase.