Enhance Image-to-Image Generation with LLaVA-generated Prompts
Zhicheng Ding, Panfeng Li, Qikai Yang, Siyang Li
TL;DR
A framework where LLaVA analyzes input images and generates textual descriptions, hereinafter LLaVA-generated prompts are proposed, where this enriched representation guides the generation process towards outputs that exhibit a stronger resemblance to the input image.
Abstract
This paper presents a novel approach to enhance image-to-image generation by leveraging the multimodal capabilities of the Large Language and Vision Assistant (LLaVA). We propose a framework where LLaVA analyzes input images and generates textual descriptions, hereinafter LLaVA-generated prompts. These prompts, along with the original image, are fed into the image-to-image generation pipeline. This enriched representation guides the generation process towards outputs that exhibit a stronger resemblance to the input image. Extensive experiments demonstrate the effectiveness of LLaVA-generated prompts in promoting image similarity. We observe a significant improvement in the visual coherence between the generated and input images compared to traditional methods. Future work will explore fine-tuning LLaVA prompts for increased control over the creative process. By providing more specific details within the prompts, we aim to achieve a delicate balance between faithfulness to the original image and artistic expression in the generated outputs.