How SPREEAI trains the model behind photorealistic virtual try-on

This post details SPREEAI's approach to training a unified diffusion model for photorealistic virtual try-on, addressing pose transfer, cloth deformation, identity preservation, and texture/logo fidelity. It describes a multi-stage training pipeline with rigorous validation gates, high-resolution training, and quantized inference using FP8. The infrastructure leverages NVIDIA H100 GPUs with InfiniBand for efficient gradient synchronization and a rebuilt storage system to manage the large volume of training artifacts. Inference optimizations include targeted upscaling for critical regions, reducing latency and peak memory usage.