The rapid evolution of text-to-image models has revolutionized digital content creation, enabling users to generate stunning visuals from simple text prompts. From DALL-E to Midjourney and Stable Diffusion, these models represent a pinnacle of generative AI, blending natural language understanding with sophisticated image synthesis. However, behind every breathtaking image lies an intricate and often painstaking training process. Developing these models is not merely about assembling the right architecture; it's about meticulously fine-tuning every aspect of their training design to achieve optimal performance, efficiency, and generalization. This deep dive explores the critical insights gained from systematic ablation studies in the context of text-to-image model training. Drawing lessons from cutting-edge research, including the development of models like PhotoRoom's PRX-1, we'll unpack how specific design choices impact model quality, training speed, and resource consu...