Enhancing Image Generation Fidelity via Progressive Prompts

• URL: http://arxiv.org/abs/2501.07070v1
• Date: Mon, 13 Jan 2025 05:48:32 GMT
• Title: Enhancing Image Generation Fidelity via Progressive Prompts
• Authors: Zhen Xiong, Yuqi Li, Chuanguang Yang, Tiao Tan, Zhihong Zhu, Siyuan Li, Yue Ma,
• Abstract summary: We propose a coarse - to - fine generation pipeline for regional prompt - following generation.<n>We find that deeper layers are always responsible for high - level content control, while shallow layers handle low - level content control.<n>Various prompts are injected into the proposed regional cross - attention control for coarse - to - fine generation.
• Score: 25.099694657440992
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The diffusion transformer (DiT) architecture has attracted significant attention in image generation, achieving better fidelity, performance, and diversity. However, most existing DiT - based image generation methods focus on global - aware synthesis, and regional prompt control has been less explored. In this paper, we propose a coarse - to - fine generation pipeline for regional prompt - following generation. Specifically, we first utilize the powerful large language model (LLM) to generate both high - level descriptions of the image (such as content, topic, and objects) and low - level descriptions (such as details and style). Then, we explore the influence of cross - attention layers at different depths. We find that deeper layers are always responsible for high - level content control, while shallow layers handle low - level content control. Various prompts are injected into the proposed regional cross - attention control for coarse - to - fine generation. By using the proposed pipeline, we enhance the controllability of DiT - based image generation. Extensive quantitative and qualitative results show that our pipeline can improve the performance of the generated images.

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