Batch-Instructed Gradient for Prompt Evolution:Systematic Prompt Optimization for Enhanced Text-to-Image Synthesis

• URL: http://arxiv.org/abs/2406.08713v1
• Date: Thu, 13 Jun 2024 00:33:29 GMT
• Title: Batch-Instructed Gradient for Prompt Evolution:Systematic Prompt Optimization for Enhanced Text-to-Image Synthesis
• Authors: Xinrui Yang, Zhuohan Wang, Anthony Hu,
• Abstract summary: This study proposes a Multi-Agent framework to optimize input prompts for text-to-image generation models. A professional prompts database serves as a benchmark to guide the instruction modifier towards generating high-caliber prompts. Preliminary ablation studies highlight the effectiveness of various system components and suggest areas for future improvements.
• Score: 3.783530340696776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-image models have shown remarkable progress in generating high-quality images from user-provided prompts. Despite this, the quality of these images varies due to the models' sensitivity to human language nuances. With advancements in large language models, there are new opportunities to enhance prompt design for image generation tasks. Existing research primarily focuses on optimizing prompts for direct interaction, while less attention is given to scenarios involving intermediary agents, like the Stable Diffusion model. This study proposes a Multi-Agent framework to optimize input prompts for text-to-image generation models. Central to this framework is a prompt generation mechanism that refines initial queries using dynamic instructions, which evolve through iterative performance feedback. High-quality prompts are then fed into a state-of-the-art text-to-image model. A professional prompts database serves as a benchmark to guide the instruction modifier towards generating high-caliber prompts. A scoring system evaluates the generated images, and an LLM generates new instructions based on calculated gradients. This iterative process is managed by the Upper Confidence Bound (UCB) algorithm and assessed using the Human Preference Score version 2 (HPS v2). Preliminary ablation studies highlight the effectiveness of various system components and suggest areas for future improvements.

Related papers

• TIPO: Text to Image with Text Presampling for Prompt Optimization [16.001151202788304]
  TIPO is an innovative framework designed to enhance text-to-image (T2I) generation by language model (LM) Unlike previous approaches that rely on Large Language Models (LLMs) or reinforcement learning (RL), TIPO adjusts user input prompts with the distribution of a trained prompt dataset.
  arXiv  Detail & Related papers  (2024-11-12T19:09:45Z)
• Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers [58.50071292008407]
  We present the first head-to-head comparison of recent discrete optimization techniques for the problem of prompt inversion. We find that focusing on the CLIP similarity between the inverted prompts and the ground truth image acts as a poor proxy for the similarity between ground truth image and the image generated by the inverted prompts.
  arXiv  Detail & Related papers  (2024-08-12T21:35:59Z)
• Multimodal Large Language Model is a Human-Aligned Annotator for Text-to-Image Generation [87.50120181861362]
  VisionPrefer is a high-quality and fine-grained preference dataset that captures multiple preference aspects. We train a reward model VP-Score over VisionPrefer to guide the training of text-to-image generative models and the preference prediction accuracy of VP-Score is comparable to human annotators.
  arXiv  Detail & Related papers  (2024-04-23T14:53:15Z)
• Dynamic Prompt Optimizing for Text-to-Image Generation [63.775458908172176]
  We introduce the textbfPrompt textbfAuto-textbfEditing (PAE) method to improve text-to-image generative models. We employ an online reinforcement learning strategy to explore the weights and injection time steps of each word, leading to the dynamic fine-control prompts.
  arXiv  Detail & Related papers  (2024-04-05T13:44:39Z)
• A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis [33.71897211776133]
  Well-designed prompts have demonstrated the potential to guide text-to-image models in generating amazing images. It is challenging for novice users to achieve the desired results by manually entering prompts. We propose a novel framework that automatically translates user-input prompts into model-preferred prompts.
  arXiv  Detail & Related papers  (2024-02-20T06:58:49Z)
• BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis [14.852061933308276]
  We propose BeautifulPrompt, a deep generative model to produce high-quality prompts from very simple raw descriptions. In our work, we first fine-tuned the BeautifulPrompt model over low-quality and high-quality collecting prompt pairs. We further showcase the integration of BeautifulPrompt to a cloud-native AI platform to provide better text-to-image generation service.
  arXiv  Detail & Related papers  (2023-11-12T06:39:00Z)
• RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model [93.8067369210696]
  Text-to-image generation (TTI) refers to the usage of models that could process text input and generate high fidelity images based on text descriptions. Diffusion models are one prominent type of generative model used for the generation of images through the systematic introduction of noises with repeating steps. In the era of large models, scaling up model size and the integration with large language models have further improved the performance of TTI models.
  arXiv  Detail & Related papers  (2023-09-02T03:27:20Z)
• SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models [56.88192537044364]
  We propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. Our approach can make text-to-image diffusion models easier to use with better user experience.
  arXiv  Detail & Related papers  (2023-05-09T05:48:38Z)
• Prompt-based Learning for Unpaired Image Captioning [86.44188293709307]
  Unpaired Image Captioning (UIC) has been developed to learn image descriptions from unaligned vision-language sample pairs. Recent successes of Vision-Language Pre-Trained Models (VL-PTMs) have triggered the development of prompt-based learning. We present in this paper a novel scheme based on prompt to train the UIC model, making best use of the powerful generalization ability.
  arXiv  Detail & Related papers  (2022-05-26T03:13:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.