  Image generation can solve insufficient labeled data issues in defect
detection. Most defect generation methods are only trained on a single product
without considering the consistencies among multiple products, leading to poor
quality and diversity of generated results. To address these issues, we propose
DefectDiffu, a novel text-guided diffusion method to model both intra-product
background consistency and inter-product defect consistency across multiple
products and modulate the consistency perturbation directions to control
product type and defect strength, achieving diversified defect image
generation. Firstly, we leverage a text encoder to separately provide
consistency prompts for background, defect, and fusion parts of the
disentangled integrated architecture, thereby disentangling defects and normal
backgrounds. Secondly, we propose the double-free strategy to generate defect
images through two-stage perturbation of consistency direction, thereby
controlling product type and defect strength by adjusting the perturbation
scale. Besides, DefectDiffu can generate defect mask annotations utilizing
cross-attention maps from the defect part. Finally, to improve the generation
quality of small defects and masks, we propose the adaptive attention-enhance
loss to increase the attention to defects. Experimental results demonstrate
that DefectDiffu surpasses state-of-the-art methods in terms of generation
quality and diversity, thus effectively improving downstream defection
performance. Moreover, defect perturbation directions can be transferred among
various products to achieve zero-shot defect generation, which is highly
beneficial for addressing insufficient data issues. The code are available at
https://github.com/FFDD-diffusion/DefectDiffu.
