人在环路的细分行业 logo 生成方法

Compared to other types of generated pictures, logos are highly abstract, diversely-designed and unified in styles, making it challenging to directly control the outcome of the generated pictures. In an effort to efficiently generate logos that are in line with the characteristics of various industries and meet the requirements of multiple designs of composition patterns, a Human-in-the-Loop field-specific logo generation method was proposed. Firstly, based on Dreambooth, a method for fine tuning text-to-image diffusion models, and a dataset composed of logos collected from publicly available online sources the text-to-image model Stable Diffusion XL was utilized as the base model and trained to develop a “prototype model” for basic logo generation. Then, groups of lexicons for targeted industries were constructed. The prototype model was then used to generate logos for targeted industries under the guidance of the lexicons. Next, via human intervention, the generated outcomes were filtered into secondary datasets tailored to industry needs. Finally, “prototype model” was iteratively fine-tuned using LoRA and the secondary datasets, obtaining the final model for logo generation. The generated results of the final model were evaluated using cosine similarity between generated images and prompt words, as well as manual questionnaire indicators. The evaluation demonstrated that the logos generated by the final model have a considerable exhibited significant improvements in industry relevance, structural integrity, and aesthetic appearance compared to those generated directly by the untrained base model.