  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.
