Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition
<|reference_start|>Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition: Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examples for training. This paper tackles two interconnected issues within this realm of personalizing text-to-image diffusion models. First, current personalization techniques fail to reliably extend to multiple concepts -- we hypothesize this to be due to the mismatch between complex scenes and simple text descriptions in the pre-training dataset (e.g., LAION). Second, given an image containing multiple personalized concepts, there lacks a holistic metric that evaluates performance on not just the degree of resemblance of personalized concepts, but also whether all concepts are present in the image and whether the image accurately reflects the overall text description. To address these issues, we introduce Gen4Gen, a semi-automated dataset creation pipeline utilizing generative models to combine personalized concepts into complex compositions along with text-descriptions. Using this, we create a dataset called MyCanvas, that can be used to benchmark the task of multi-concept personalization. In addition, we design a comprehensive metric comprising two scores (CP-CLIP and TI-CLIP) for better quantifying the performance of multi-concept, personalized text-to-image diffusion methods. We provide a simple baseline built on top of Custom Diffusion with empirical prompting strategies for future researchers to evaluate on MyCanvas. We show that by improving data quality and prompting strategies, we can significantly increase multi-concept personalized image generation quality, without requiring any modifications to model architecture or training algorithms.<|reference_end|>
@article{yeh2024gen4gen:,
    title={Gen4Gen: Generative Data Pipeline for Generative Multi-Concept
  Composition},
    author={Chun-Hsiao Yeh, Ta-Ying Cheng, He-Yen Hsieh, Chuan-En Lin, Yi Ma,
  Andrew Markham, Niki Trigoni, H.T. Kung, Yubei Chen},
    journal={arXiv preprint arXiv:2402.15504},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15504},
    primaryClass={cs.CV cs.AI}
}