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.

通过利用大型语言与视觉助手（LLaVA）的多模态能力，本文提出了一种增强图像 - 图像生成的新方法。LLaVA 分析输入图像并生成文本描述，即 LLaVA 生成的提示。这些提示与原始图像一起输入到图像 - 图像生成流程中，丰富的表示指导生成过程以展现更强的输入图像相似性。广泛的实验证明了 LLaVA 生成的提示在促进图像相似性方面的有效性。与传统方法相比，我们观察到生成图像和输入图像之间视觉一致性的显著改进。未来的工作将探索对 LLaVA 提示进行微调，以更好地控制创造过程。通过在提示中提供更具体的细节，我们旨在在生成的输出中实现对原始图像的忠实性和艺术表现之间的微妙平衡。

利用 LLaVA 提示和负面提示提升图像生成

Enhance Image-to-Image Generation with LLaVA Prompt and Negative Prompt

With the development of image generation technology, AI-based image
generation has been applied in various fields. However, the development of AIGC
image generative models also brings new problems and challenges. A significant
challenge is that AI-generated images (AIGI) compared to natural images may
have some unique distortions, and not all generated images meet the
requirements of the real world, so it is of great significance to evaluate
AI-generated images more comprehensively. Although previous work has
established some human perception-based AIGC image quality assessment databases
for text-generated images, the AI image generation technology includes
scenarios like text-to-image and image-to-image, and assessing only the images
generated by text-to-image models is insufficient. To address this issue, we
have established a human perception-based image-to-image AIGC image quality
assessment database, named PKU-I2IQA. We conducted a comprehensive analysis of
the PKU-I2IQA database. Furthermore, we introduced two benchmark models:
NR-AIGCIQA based on no-reference image quality assessment and FR-AIGCIQA based
on full-reference image quality assessment.Finally, leveraging this database,
we conducted benchmark experiments and compared the performance of the proposed
benchmark models. The PKU-I2IQA database and benchmarks will be released to
facilitate future research on this https URL
Keywords: AIGC, image-to-image generation, image quality assessment,
NR-AIGCIQA, FR-AIGCIQA

基于人类感知的图像到图像 AIGC 图像质量评估数据库 PKU-I2IQA 的建立，引入两个基准模型：基于无参考图像质量评估的 NR-AIGCIQA 和基于全参考图像质量评估的 FR-AIGCIQA，并通过基准实验比较了两个模型的性能。