All-in-One Conditioning for Text-to-Image Synthesis

• URL: http://arxiv.org/abs/2602.09165v1
• Date: Mon, 09 Feb 2026 20:16:19 GMT
• Title: All-in-One Conditioning for Text-to-Image Synthesis
• Authors: Hirunima Jayasekara, Chuong Huynh, Yixuan Ren, Christabel Acquaye, Abhinav Shrivastava,
• Abstract summary: We propose a novel approach that grounds text-to-image synthesis within the framework of scene graph structures.<n>We introduce a zero-shot, scene graph-based conditioning mechanism that generates soft visual guidance during inference.<n>This enables the model to maintain text-image alignment while supporting lightweight, coherent, and diverse image synthesis.
• Score: 45.22434803596108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate interpretation and visual representation of complex prompts involving multiple objects, attributes, and spatial relationships is a critical challenge in text-to-image synthesis. Despite recent advancements in generating photorealistic outputs, current models often struggle with maintaining semantic fidelity and structural coherence when processing intricate textual inputs. We propose a novel approach that grounds text-to-image synthesis within the framework of scene graph structures, aiming to enhance the compositional abilities of existing models. Eventhough, prior approaches have attempted to address this by using pre-defined layout maps derived from prompts, such rigid constraints often limit compositional flexibility and diversity. In contrast, we introduce a zero-shot, scene graph-based conditioning mechanism that generates soft visual guidance during inference. At the core of our method is the Attribute-Size-Quantity-Location (ASQL) Conditioner, which produces visual conditions via a lightweight language model and guides diffusion-based generation through inference-time optimization. This enables the model to maintain text-image alignment while supporting lightweight, coherent, and diverse image synthesis.

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