Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis

Understand

Large-scale diffusion models have achieved state-of-the-art results on text-to-image synthesis (T2I) tasks.

• Despite their ability to generate high-quality yet creative images, we observe that attribution-binding and compositional capabilities are still considered major challenging issues, especially when involving multiple objects.
• In this work, we improve the compositional skills of T2I models, specifically more accurate attribute binding and better image compositions.
• To do this, we incorporate linguistic structures with the diffusion guidance process based on the controllable properties of manipulating cross-attention layers in diffusion-based T2I models.