RefAdapt-DiT: Adaptive Joint Attention for Reference-Conditioned Diffusion Transformers

arXiv:2609.32415 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

Transformer — 18× in this paper · explained in 2 episodes
Benchmark — 11× in this paper · explained in 2 episodes
Ablation — 9× in this paper · explained in 1 episode
Diffusion Model — 9× in this paper · explained in 8 episodes
Joint Attention — 7× in this paper · explained in 2 episodes
Alignment — 6× in this paper · explained in 1 episode
Consistency — 6× in this paper · explained in 3 episodes
Diffusion Models — 6× in this paper · explained in 6 episodes
Fidelity — 5× in this paper · explained in 2 episodes
Redundancy — 5× in this paper · explained in 2 episodes
Temporal consistency — 4× in this paper · explained in 2 episodes
Aggregation — 2× in this paper · explained in 1 episode
Calibration — 2× in this paper · explained in 7 episodes
Ablation Studies — 1× in this paper · explained in 1 episode
Generative AI — 1× in this paper · explained in 2 episodes
Generative Modeling — 1× in this paper · explained in 3 episodes
In-Context Learning — 1× in this paper · explained in 4 episodes
Trace — 1× in this paper · explained in 2 episodes
Transformer Models — 1× in this paper · explained in 2 episodes

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