PRX Part 3: 24h Training of Text-to-Image Diffusion in Pixel Space (x-prediction, TREAD, REPA, DINOv3, Muon)

AI Impact Summary

The post demonstrates a 24-hour, end-to-end training recipe for a text-to-image diffusion model in pixel space, combining x-prediction with perceptual losses (LPIPS, DINOv3), token routing (TREAD), and representation alignment (REPA with DINOv3) running on Muon with FSDP. It details concrete architectural choices (patch size 32, 512px start, 1024px fine-tune; 256 bottleneck) and a compute plan (32 H200 GPUs ~ $1.5k) to push performance within a single day, and highlights open-source code for reproducibility. This signals a mature, repeatable pathway for rapid domain-specific model prototyping, but it hinges on substantial GPU capacity and careful orchestration across multiple components to achieve reliable results.