Sinha, A., Sun, B., Kalia, A., Casanova, A., Blanchard, E., Yan, D., Zhang, W., Nelli, T., Chen, J., Shah, H., Yu, L., Singh, M.K., Ramchandani, A., Sanjabi, M., Gupta, S., Bearman, A., & Mahajan, D. (2024). Text-to-Sticker: Style Tailoring Latent Diffusion Models for Human Expression. arXiv preprint arXiv:2311.10794v2.
This paper aims to address the challenge of fine-tuning pre-trained text-to-image LDMs for generating high-quality stickers that exhibit strong adherence to text prompts, consistency in visual style, and diversity in scene composition.
The researchers propose a multi-stage fine-tuning approach called "Style Tailoring." They start with a pre-trained LDM (Emu-256) and fine-tune it using three datasets: a large, weakly-aligned sticker domain dataset for domain adaptation, a human-annotated dataset (HITL) for prompt alignment, and an expert-curated dataset (EITL) for style alignment. The Style Tailoring method involves training the model on the HITL dataset for initial denoising steps to ensure prompt alignment and then on the EITL dataset for later steps to refine the style.
The Style Tailoring method offers a practical and effective approach for adapting large-scale LDMs to specialized domains like sticker generation, enabling the creation of high-quality, diverse, and semantically aligned visual content.
This research contributes to the field of text-to-image generation by presenting a novel fine-tuning strategy that addresses the limitations of existing methods in balancing prompt alignment and style consistency. It highlights the importance of carefully curated datasets and phased training for achieving optimal results in domain-specific image generation tasks.
The study acknowledges the limitations posed by the foundational text-to-image model's pre-training data and the subjective nature of human evaluation. Future research could explore methods for mitigating these limitations and further enhance the model's ability to generate images of rare or unseen concepts. Additionally, investigating the application of Style Tailoring to other domains and image generation tasks would be valuable.
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by Animesh Sinh... at arxiv.org 10-04-2024
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