Prizm 1

Research Model

Prizm is our experimental venture into visual and latent generation, combining Variational Autoencoders (VAEs) with diffusion processes to study latent space compression, noise scheduling, and high-fidelity iterative denoising.

Overview

Prizm serves as our primary testbed for generative visual modeling. By coupling a robust VAE for efficient image compression with a scalable diffusion backbone, the goal is straightforward: understand how different latent dimensions, denoising architectures, and training dynamics impact the stability and quality of generated outputs.

Research Focus

  • Stability, expressiveness, and compression rates of the VAE latent space
  • Effects of varied noise schedules on diffusion generation quality
  • Scaling behavior of the denoising network (e.g., U-Net or Transformer-based architectures)
  • Failure modes in complex spatial coherence and artifact reduction

Intended Use

Prizm is an internal research model. It is not publicly accessible and is not optimized for deployment. Its role is to inform decisions about generative architectures and training methodologies that will eventually apply to production-oriented visual work.