  The state-of-the-art methods for e-commerce product background generation
suffer from the inefficiency of designing product-wise prompts when scaling up
the production, as well as the ineffectiveness of describing fine-grained
styles when customizing personalized backgrounds for some specific brands. To
address these obstacles, we integrate the category commonality and personalized
style into diffusion models. Concretely, we propose a Category-Wise Generator
to enable large-scale background generation for the first time. A unique
identifier in the prompt is assigned to each category, whose attention is
located on the background by a mask-guided cross attention layer to learn the
category-wise style. Furthermore, for products with specific and fine-grained
requirements in layout, elements, etc, a Personality-Wise Generator is devised
to learn such personalized style directly from a reference image to resolve
textual ambiguities, and is trained in a self-supervised manner for more
efficient training data usage. To advance research in this field, the first
large-scale e-commerce product background generation dataset BG60k is
constructed, which covers more than 60k product images from over 2k categories.
Experiments demonstrate that our method could generate high-quality backgrounds
for different categories, and maintain the personalized background style of
reference images. The link to BG60k and codes will be available soon.
