  The task of steel surface defect recognition is an industrial problem with
great industry values. The data insufficiency is the major challenge in
training a robust defect recognition network. Existing methods have
investigated to enlarge the dataset by generating samples with generative
models. However, their generation quality is still limited by the insufficiency
of defect image samples. To this end, we propose Stable Surface Defect
Generation (StableSDG), which transfers the vast generation distribution
embedded in Stable Diffusion model for steel surface defect image generation.
To tackle with the distinctive distribution gap between steel surface images
and generated images of the diffusion model, we propose two processes. First,
we align the distribution by adapting parameters of the diffusion model,
adopted both in the token embedding space and network parameter space. Besides,
in the generation process, we propose image-oriented generation rather than
from pure Gaussian noises. We conduct extensive experiments on steel surface
defect dataset, demonstrating state-of-the-art performance on generating
high-quality samples and training recognition models, and both designed
processes are significant for the performance.
