  Generative models have increasingly impacted relative tasks, from computer
vision to interior design and other fields. Stable diffusion is an outstanding
diffusion model that paves the way for producing high-resolution images with
thorough details from text prompts or reference images. It will be an
interesting topic about gaining improvements for small datasets with
image-sparse categories. This study utilized seven common categories and three
widespread weed species to evaluate the efficiency of a stable diffusion model.
In detail, Stable diffusion was used to generate synthetic images belonging to
these classes; three techniques (i.e., Image-to-image translation, Dreambooth,
and ControlNet) based on stable diffusion were leveraged for image generation
with different focuses. Then, classification and detection tasks were conducted
based on these synthetic images, whose performance was compared to the models
trained on original images. Promising results have been achieved in some
classes. This seminal study may expedite the adaption of stable diffusion
models to different fields.
