  Anomaly synthesis is one of the effective methods to augment abnormal samples
for training. However, current anomaly synthesis methods predominantly rely on
texture information as input, which limits the fidelity of synthesized abnormal
samples. Because texture information is insufficient to correctly depict the
pattern of anomalies, especially for logical anomalies. To surmount this
obstacle, we present the AnomalyXFusion framework, designed to harness
multi-modality information to enhance the quality of synthesized abnormal
samples. The AnomalyXFusion framework comprises two distinct yet synergistic
modules: the Multi-modal In-Fusion (MIF) module and the Dynamic Dif-Fusion
(DDF) module. The MIF module refines modality alignment by aggregating and
integrating various modality features into a unified embedding space, termed
X-embedding, which includes image, text, and mask features. Concurrently, the
DDF module facilitates controlled generation through an adaptive adjustment of
X-embedding conditioned on the diffusion steps. In addition, to reveal the
multi-modality representational power of AnomalyXFusion, we propose a new
dataset, called MVTec Caption. More precisely, MVTec Caption extends 2.2k
accurate image-mask-text annotations for the MVTec AD and LOCO datasets.
Comprehensive evaluations demonstrate the effectiveness of AnomalyXFusion,
especially regarding the fidelity and diversity for logical anomalies. Project
page: http:github.com/hujiecpp/MVTec-Caption
