sihyun-yu/REPA

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1700 stars · 98 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

REPA is the official PyTorch implementation of the ICLR 2025 paper 'Representation Alignment for Generation', a regularization technique that aligns diffusion transformer representations with pretrained self-supervised visual encoders. It dramatically accelerates training of diffusion models like SiT (17.5x faster convergence) and achieves state-of-the-art image generation quality (FID=1.42 on ImageNet 256x256).

Use cases

• train diffusion transformers faster with representation alignment
• reproduce REPA results on ImageNet 256x256
• improve generation quality of SiT or DiT models
• apply representation alignment regularization to diffusion model training
• research denoising-based generative models
• benchmark image generation FID scores

When to choose

• you are training diffusion transformers or flow-based models and want faster convergence
• you need state-of-the-art class-conditional ImageNet generation results
• you are doing research on representation learning in diffusion models

When to avoid

• you need a production-ready image generation service rather than research training code
• you want text-to-image generation out of the box (experiments are ImageNet class-conditional)
• you need a general-purpose diffusion library like Diffusers

Facets

library · maturity active

machine-learning deep-learning image-processing deep-learning machine-learning image-processing python diffusion-models diffusion-transformers representation-alignment research-code image-generation iclr-2025 training-efficiency gpu linux

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