Jewel-T2I: Learning Jewelry Generation from Expert Seed Data

T2I generation can support jewelry customization by visualizing design requirements before physical prototyping, but general models often confuse craft structures or fail to preserve the requested motifs and materials. We present Jewel-T2I, which constructs training data from expert jewelry assets for domain adaptation. Its data pipeline, JewelGen, expands 500 seed assets through semantic and viewpoint editing, while visual craft exemplars guide the synthesis of plain, filigree, and enamel designs. MLLM screening and designer verification select 20K image-text pairs for adapting the diffusion model. The resulting generator is combined with a prompt model trained on a separate curated jewelry corpus, allowing users to specify design requirements without supplying reference images at inference. We also introduce JewelBench, a benchmark of 2K entries that evaluates AF, CF, and AQ across the three crafts. With identical prompts, the adapted generator improves over Qwen-Image by 6.3, 8.6, and 5.0 points on these criteria, respectively. The complete system achieves the highest mean scores among the compared systems across all three crafts under both automated and human evaluation. These results show that the constructed training data improves the ability of T2I models to follow jewelry design requirements on JewelBench.