Compositional Text-to-Image Generation with Dense Blob Representations

• URL: http://arxiv.org/abs/2405.08246v1
• Date: Tue, 14 May 2024 00:22:06 GMT
• Title: Compositional Text-to-Image Generation with Dense Blob Representations
• Authors: Weili Nie, Sifei Liu, Morteza Mardani, Chao Liu, Benjamin Eckart, Arash Vahdat,
• Abstract summary: Existing text-to-image models struggle to follow complex text prompts. We develop a blob-grounded text-to-image diffusion model, termed BlobGEN, for compositional generation. Our experiments show that BlobGEN achieves superior zero-shot generation quality and better layout-guided controllability on MS-COCO.
• Score: 48.1976291999674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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