HiRAE: Hierarchical Representation Autoencoding with Residual Budgets

arXiv:2609.37775 · cs.CV, cs.AI · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-29

Updated: 2026-09-29

Code: https://github.com/black-forest-labs/flux

Terminology

• Alignment — 13× in this paper · explained in 1 episode
• Aggregation — 11× in this paper · explained in 1 episode
• Fidelity — 11× in this paper · explained in 2 episodes
• Regularization — 11× in this paper · explained in 3 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Diffusion Models — 7× in this paper · explained in 6 episodes
• Fine-Tuning — 7× in this paper · explained in 5 episodes
• Transformer — 6× in this paper · explained in 2 episodes
• Adaptation — 5× in this paper · explained in 1 episode
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Inception Score — 2× in this paper
• Quantization — 2× in this paper · explained in 12 episodes
• Supervised Fine-Tuning (SFT) — 2× in this paper · explained in 8 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment Scores — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Tokenization — 1× in this paper · explained in 2 episodes

Sources

• Hyperspherical Autoencoder for High-Fidelity Image Reconstruction and Generation
• BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
• IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
• One Layer Is Enough: Adapting Pretrained Visual Encoders for Image Generation
• GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment
• RPiAE: A Representation-Pivoted Autoencoder Enhancing Both Image Generation and Editing
• ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
• REPA-E: Unlocking VAE for End-to-End Tuning with Latent Diffusion Transformers
• Improving Reconstruction of Representation Autoencoder
• DINOv3
• Improved Baselines with Representation Autoencoders
• JourneyDB: A Benchmark for Generative Image Understanding
• DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders
• Representation Fr'echet Loss for Visual Generation
• Diffusion Transformers with Representation Autoencoders
• Diffusing in the Right Space: A Systematic Study of Latent Diffusability
• Beyond the Last Layer: Multi-Layer Representation Fusion for Visual Tokenization

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