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We introduce Style Tailoring, a recipe to finetune Latent Diffusion Models(LDMs) in a distinct domain with high visual quality, prompt alignment andscene diversity. We choose sticker image generation as the target domain, asthe images significantly differ from photorealistic samples typically generatedby large-scale LDMs. We start with a competent text-to-image model, like Emu,and show that relying on prompt engineering with a photorealistic model togenerate stickers leads to poor prompt alignment and scene diversity. Toovercome these drawbacks, we first finetune Emu on millions of sticker-likeimages collected using weak supervision to elicit diversity. Next, we curatehuman-in-the-loop (HITL) Alignment and Style datasets from model generations,and finetune to improve prompt alignment and style alignment respectively.Sequential finetuning on these datasets poses a tradeoff between better stylealignment and prompt alignment gains. To address this tradeoff, we propose anovel fine-tuning method called Style Tailoring, which jointly fits the contentand style distribution and achieves best tradeoff. Evaluation results show ourmethod improves visual quality by 14%, prompt alignment by 16.2% and scenediversity by 15.3%, compared to prompt engineering the base Emu model forstickers generation.

Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression