Text-to-Image Diffusion with Complex and Detailed Prompts
Abstract
Diffusion-based generative models have significantly ascended to the prominence providing massive leap forward in the field of computer vision, emerging as a key tool to unlock the vast potential of generative AI capabilities. Among their extensive applications in several potential fields, their ability to generate high quality images from textual prompts is remarkable. This intersection of text and image synthesis has the potential to revolutionize content creation, design, and various other domains where conveying complex visual concepts from textual descriptions is paramount. However, despite their remarkable achievements, diffusion-based generative models encounter notable hurdles when tasked with processing lengthy and intricate textual prompts. These challenges become particularly pronounced when describing scenes with multiple objects, intricate attributes, and nuanced contextual details. While these models excel in faithfully generating images from succinct, single-object descriptions, they often struggle to capture the richness and complexity inherent in longer textual inputs. This limitation poses a significant barrier, hindering their ability to accurately translate intricate textual descriptions into coherent visual representations. To mitigate these issues, in this work, we present a novel training-free approach leveraging a Large Language Models (LLMs) to extract critical components from text prompts, including bounding box coordinates for foreground objects,…
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