Diffusion Transformers with REG Enhancement: Implementation and Evaluation on CIFAR-10
Abstract
This work presents a comprehensive implementation and theoretical analysis of Diffusion Transformers enhanced with Representation Entanglement for Generation (REG). We investigate whether Vision Transformers can effectively replace the conventional U-Net backbone in diffusion models for small-scale image generation tasks. Key Features:• Complete PyTorch implementation of Scalable Interpolant Transformer (SiT) with velocity prediction• REG integration using frozen pretrained DINOv2 models for semantic guidance• Fair comparison: U-Net baseline (32.3M params), SiT baseline (32.6M params), REG-SiT (32.7M params)• Training on CIFAR-10 cats vs dogs subset (9K train, 1K val, 2K test)• 20 epochs training with identical hyperparameters across all models Results:• U-Net: FID 322.41, Best Loss 0.1449• SiT: FID 471.50, Best Loss 0.2000• REG-SiT: FID 471.04, Best Loss 0.2012 (97.4% alignment loss reduction)
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