On the Diffusibility of High-Dimensional Latents

arXiv:2609.28473 · cs.CV, cs.LG · Submitted 2026-09-23 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-23

Updated: 2026-09-23

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

Project page: https://cfeng16.github.io/on_the_diffusibility

Terminology

• Diffusion Model — 28× in this paper · explained in 8 episodes
• Diffusion Models — 19× in this paper · explained in 6 episodes
• Transformer — 16× in this paper · explained in 2 episodes
• Flow Matching — 14× in this paper · explained in 8 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Latent Space — 12× in this paper · explained in 7 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Generative Modeling — 5× in this paper · explained in 3 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Latent Diffusion Model — 4× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• Representation Learning — 3× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Structural Integrity — 2× in this paper · explained in 3 episodes
• alpha — 2× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Activation Function — 1× in this paper · explained in 1 episode
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Entanglement — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Intrinsic Dimension — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Linear Probing — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Semantic Understanding — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Qwen3-VL Technical Report
• Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
• AlignTok: Aligning Visual Foundation Encoders to Tokenizers for Diffusion Models
• BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
• Are We on the Right Way for Evaluating Large Vision-Language Models?
• Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• A Neural Algorithm of Artistic Style
• Learnings from Scaling Visual Tokenizers for Reconstruction and Generation
• ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
• Auto-Encoding Variational Bayes
• EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
• Back to Basics: Let Denoising Generative Models Denoise
• Flow Matching for Generative Modeling
• Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
• Decoupled Weight Decay Regularization
• MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
• DINOv2: Learning Robust Visual Features without Supervision
• Transfer between Modalities with MetaQueries
• Hierarchical Text-Conditional Image Generation with CLIP Latents

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