Compositional Text-to-Image Generation with Dense Blob Representations
<|reference_start|>Compositional Text-to-Image Generation with Dense Blob Representations: Existing text-to-image models struggle to follow complex text prompts, raising the need for extra grounding inputs for better controllability. In this work, we propose to decompose a scene into visual primitives - denoted as dense blob representations - that contain fine-grained details of the scene while being modular, human-interpretable, and easy-to-construct. Based on blob representations, we develop a blob-grounded text-to-image diffusion model, termed BlobGEN, for compositional generation. Particularly, we introduce a new masked cross-attention module to disentangle the fusion between blob representations and visual features. To leverage the compositionality of large language models (LLMs), we introduce a new in-context learning approach to generate blob representations from text prompts. Our extensive experiments show that BlobGEN achieves superior zero-shot generation quality and better layout-guided controllability on MS-COCO. When augmented by LLMs, our method exhibits superior numerical and spatial correctness on compositional image generation benchmarks. Project page: https://blobgen-2d.github.io.<|reference_end|>
@article{nie2024compositional,
    title={Compositional Text-to-Image Generation with Dense Blob Representations},
    author={Weili Nie, Sifei Liu, Morteza Mardani, Chao Liu, Benjamin Eckart,
  Arash Vahdat},
    journal={arXiv preprint arXiv:2405.08246},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08246},
    primaryClass={cs.CV cs.AI cs.LG}
}