The field of text-to-image (T2I) generation has garnered significant
attention both within the research community and among everyday users. Despite
the advancements of T2I models, a common issue encountered by users is the need
for repetitive editing of input prompts in order to receive a satisfactory
image, which is time-consuming and labor-intensive. Given the demonstrated text
generation power of large-scale language models, such as GPT-k, we investigate
the potential of utilizing such models to improve the prompt editing process
for T2I generation. We conduct a series of experiments to compare the common
edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting
T2I, and examine factors that may influence this process. We found that GPT-k
models focus more on inserting modifiers while humans tend to replace words and
phrases, which includes changes to the subject matter. Experimental results
show that GPT-k are more effective in adjusting modifiers rather than
predicting spontaneous changes in the primary subject matters. Adopting the
edit suggested by GPT-k models may reduce the percentage of remaining edits by
20-30%.

该研究使用 GPT-k 等大型语言模型来改进文本到图像生成中的提示编辑过程，发现 GPT-k 模型更专注于插入修改词语，从而减少 20-30% 的剩余编辑。