Multivariate Diffusion Transformer with Decoupled Attention for High-Fidelity Mask-Text Collaborative Facial Generation

arXiv:2511.12631 · cs.CV, cs.AI · Submitted 2025-11-16 · Read on arXiv

cs.CV, cs.AI

Submitted: 2025-11-16

Updated: 2026-08-27

Code: https://github.com/black-forestlabs/flux

Terminology

• Transformer — 31× in this paper · explained in 2 episodes
• Diffusion Model — 23× in this paper · explained in 8 episodes
• Diffusion Models — 20× in this paper · explained in 6 episodes
• Attention Mechanism — 16× in this paper · explained in 2 episodes
• Fidelity — 16× in this paper · explained in 2 episodes
• Generalization — 11× in this paper · explained in 13 episodes
• Computational overhead — 10× in this paper · explained in 2 episodes
• Consistency — 10× in this paper · explained in 3 episodes
• Tokenization — 10× in this paper · explained in 2 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Latent Space — 8× in this paper · explained in 7 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Robustness — 7× in this paper · explained in 5 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Adaptation — 4× in this paper · explained in 1 episode
• Generative Adversarial Network — 4× in this paper
• Benchmark — 3× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Flow Matching — 3× in this paper · explained in 8 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Latent Diffusion Models — 3× in this paper · explained in 2 episodes
• Semantic Alignment — 3× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Decoupling — 2× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Image Reward Score — 2× in this paper
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• DINO Structure Distance — 1× in this paper
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Dynamic Weighting — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Learned Perceptual Image Patch Similarity — 1× in this paper
• LoRA Fine-Tuning — 1× in this paper · explained in 3 episodes
• Multimodal Integration — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• LoRA: Low-Rank Adaptation of Large Language Models
• ControlNeXt: Powerful and Efficient Control for Image and Video Generation
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• Denoising Diffusion Implicit Models
• OminiControl: Minimal and Universal Control for Diffusion Transformer
• OminiControl2: Efficient Conditioning for Diffusion Transformers
• ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation

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