Progress Update on My VAE+DDPM Implementation for CIFAR-10
After successfully implementing VAE+DDPM for MNIST digit generation with category-specific sampling (as I described in my previous article), I've been working on adapting the same approach to the more challenging CIFAR-10 dataset.
The CIFAR-10 Challenge
As expected, CIFAR-10 presents a significant step up in complexity compared to MNIST:
- More channels (RGB vs. grayscale)
- Larger image dimensions
- Much greater visual complexity
While I've maintained the core architecture of my previous model, these differences have required extensive parameter tuning and experimentation.
The Critical Importance of VAE Tuning
With this VAE+DDPM approach, the quality of the Variational Autoencoder is foundational to the entire process. If the VAE doesn't properly encode and reconstruct images, the subsequent DDPM sampling won't produce good results regardless of how well it's tuned.
This reality has led me to spend the majority of my time carefully adjusting the VAE parameters, meticulously comparing original images against VAE reconstructions to ensure fidelity.
Current Progress
I'm pleased to report that training is progressing smoothly, though there's still work to be done. The current reconstructions show some blurriness, but the core structures and colors are being preserved reasonably well.
This intermediate checkpoint gives me confidence that I'm on the right track, though more fine-tuning will be necessary to achieve the level of clarity I'm aiming for.
Next Steps
In the coming weeks, I'll continue refining the VAE to improve reconstruction quality before moving on to optimizing the DDPM component. I'll share another update once I've achieved more substantial improvements.
Have you worked with VAE+DDPM architectures for image generation? I'd love to hear about your experiences in the comments below!