  In this paper, we delve into a new task known as small object editing (SOE),
which focuses on text-based image inpainting within a constrained, small-sized
area. Despite the remarkable success have been achieved by current image
inpainting approaches, their application to the SOE task generally results in
failure cases such as Object Missing, Text-Image Mismatch, and Distortion.
These failures stem from the limited use of small-sized objects in training
datasets and the downsampling operations employed by U-Net models, which
hinders accurate generation. To overcome these challenges, we introduce a novel
training-based approach, SOEDiff, aimed at enhancing the capability of baseline
models like StableDiffusion in editing small-sized objects while minimizing
training costs. Specifically, our method involves two key components: SO-LoRA,
which efficiently fine-tunes low-rank matrices, and Cross-Scale Score
Distillation loss, which leverages high-resolution predictions from the
pre-trained teacher diffusion model. Our method presents significant
improvements on the test dataset collected from MSCOCO and OpenImage,
validating the effectiveness of our proposed method in small object editing. In
particular, when comparing SOEDiff with SD-I model on the OpenImage-f dataset,
we observe a 0.99 improvement in CLIP-Score and a reduction of 2.87 in FID.
