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Photoroom shares systematic ablation studies on training efficient text-to-image models from scratch, documenting what actually improves convergence and training speed. Key findings: representation alignment (REPA) with frozen vision encoders significantly boosts early training; better latent spaces (REPA-E, FLUX2-AE) provide major quality gains; x-prediction enables stable 1024×1024 pixel-space training; token routing (TREAD/SPRINT) becomes crucial at high resolutions; long descriptive captions dramatically outperform short ones; synthetic data accelerates early structure learning while real images better match photographic textures. Practical tips include using Muon optimizer for faster convergence and avoiding BF16 weight storage. The post establishes a baseline PRX-1.2B model trained with Flow Matching and evaluates each technique using FID, CMMD, and DINO-MMD metrics.

Training Design for Text-to-Image Models: Lessons from Ablations

Training Objectives: Beyond Vanilla Flow Matching

Token Routing and Sparsification to Reduce Compute Costs