I’m training a latent diffusion model and wondering what the scaling laws were, I couldnt find much info online. My current is around 430M parameters training on around 2 Million images. I’m thinking of possibly scaling the parameters up a bit to increase the quality. Is this a good idea or should I scale down my images or parameters? Based on SD, they seemed to use billions for their models which were at 860M, half of mine
AI-enabled cheating is forcing some schools to go analog