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1The University of Hong Kong
2Nanjing University 3University of Chinese Academy of Sciences
4Nanyang Technological University 5Harbin Institute of Technology
We propose TACA, a parameter-efficient method that dynamically rebalances cross-modal attention in multimodal diffusion transformers to improve text-image alignment.
teaser.mp4
Usage
For Stable Diffusion 3.5, simply run:
python infer/infer_sd3.py
For FLUX.1, run:
python infer/infer_flux.py
Benchmark
Comparison of alignment evaluation on T2I-CompBench for FLUX.1-Dev-based and SD3.5-Medium-based models.
Model
Attribute Binding
Object Relationship
Complex $\uparrow$
Color $\uparrow$
Shape $\uparrow$
Texture $\uparrow$
Spatial $\uparrow$
Non-Spatial $\uparrow$
FLUX.1-Dev
0.7678
0.5064
0.6756
0.2066
0.3035
0.4359
FLUX.1-Dev + TACA ($r = 64$)
0.7843
0.5362
0.6872
0.2405
0.3041
0.4494
FLUX.1-Dev + TACA ($r = 16$)
0.7842
0.5347
0.6814
0.2321
0.3046
0.4479
SD3.5-Medium
0.7890
0.5770
0.7328
0.2087
0.3104
0.4441
SD3.5-Medium + TACA ($r = 64$)
0.8074
0.5938
0.7522
0.2678
0.3106
0.4470
SD3.5-Medium + TACA ($r = 16$)
0.7984
0.5834
0.7467
0.2374
0.3111
0.4505
Showcases
About
[ICCV25] TACA: Rethinking Cross-Modal Interaction in Multimodal Diffusion Transformers
