# Progressive Text-to-Image Diffusion with Soft Latent Direction

**Authors:** YuTeng Ye, Jiale Cai, Hang Zhou, Guanwen Li, Youjia Zhang, Zikai Song, Chenxing Gao, Junqing Yu, Wei Yang  
**arXiv:** [2309.09466](https://arxiv.org/abs/2309.09466)  
**Date:** 2023-09-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.09466v1/

## Key points

- The paper addresses challenges in text-to-image generation, specifically synthesizing and manipulating multiple entities while adhering to spatial and relational constraints.
- The authors propose a progressive synthesis and editing operation that incorporates entities into the target image, ensuring adherence to constraints at each step.
- Pre-trained text-to-image diffusion models struggle with handling a greater number of entities, so the authors leverage a Large Language Model (LLM) to decompose complex text descriptions into coherent directives.
- The Stimulus, Response, and Fusion (SRF) framework is introduced to facilitate executing directives involving distinct semantic operations such as insertion, editing, and erasing.
- The proposed framework demonstrates significant advancements in object synthesis from intricate and lengthy textual inputs.
- It establishes a new benchmark for text-to-image generation tasks and raises performance standards in the field.

## Summary

The paper titled "Progressive Text-to-Image Diffusion with Soft Latent Direction" addresses the challenges in text-to-image generation, specifically in synthesizing and manipulating multiple entities while adhering to spatial and relational constraints. The authors propose an innovative progressive synthesis and editing operation that systematically incorporates entities into the target image, ensuring their adherence to constraints at each step. The authors observe that pre-trained text-to-image diffusion models can handle one or two entities effectively but struggle when dealing with a greater number. To overcome this limitation, the authors leverage the capabilities of a Large Language Model (LLM) to decompose complex text descriptions into coherent directives following strict formats. To facilitate the execution of directives involving distinct semantic operations such as insertion, editing, and erasing, the authors introduce the Stimulus, Response, and Fusion (SRF) framework. This framework gently stimulates latent regions in alignment with each operation and then fuses the responsive latent components to achieve cohesive entity manipulation. The proposed framework demonstrates significant advancements in object synthesis when faced with intricate and lengthy textual inputs. It establishes a new benchmark for text-to-image generation tasks and raises performance standards in the field. Overall, this paper presents a novel approach to address the challenges of synthesizing and manipulating multiple entities in text-to-image generation. The proposed progressive synthesis and editing operation combined with the SRF framework offer promising results for improving object synthesis from textual descriptions.