# Inv-ReVersion: Enhanced Relation Inversion Based on Text-to-Image Diffusion Models
## 2 Related Work  
Text-to-image diffusion models. Diffusion models [17; 18; 19; 20] have become a mainstream method in the field of image generation [21; 22; 23; 24; 25; 26; 27; 28] due to their ability to produce high-quality images, sparking a variety of innovative applications [29; 30; 31; 32; 33; 34]. The pioneering work of this method was initially introduced by [PERSON] et al. [18], who demonstrated how to use the diffusion process to simulate the gradual transition from random noise to data distribution in image generation. Subsequently, latent diffusion models (LDM) [1] proposed executing the diffusion process in the latent image space, effectively reducing the computational cost of generating high-resolution images. Text-to-image (T2I) diffusion models achieve advanced image generation performance by combining pre-trained language models, such as CLIP [35], to encode text into latent vectors. Among this series of models, Stable Diffusion, based on the LDM framework, marks the current state-of-the-art (SOTA) in this field. Our research primarily conducts experiments on the Stable Diffusion model to explore its potential in understanding and generating images with specific relations.  
Diffusion-based inversion techniques mainly embed images into the model's latent space by adding noise to the images and then using the reverse denoising process of the diffusion models [29; 30]. However, this process may cause significant changes to the model's latent space to adapt to the new data distribution, sometimes even leading to catastrophic forgetting in the model. To overcome this challenge, the Textual Inversion technique [6] was proposed. It aims to find a new pseudo-word in the text embedding space of the diffusion model that represents a specific concept of the image. This new word can be combined with other text prompts to form new sentences, enabling more personalized image creation. The advantage of this type of inversion method [6; 7; 31; 36] is that it can leverage the rich semantic knowledge already learned by the text pre-training model, thus achieving model fine-tuning in an intuitive and efficient way.  
ReVersion aims to extract a common relation concept \(\langle R\rangle\) from several exemplar images through the Relation Inversion method. Its core idea is based on the \"Preposition Prior\", which sparsely activates relation prompts in the real world on a basic set of prepositions. Based on this idea, ReVersion adopts a novel relation-steer contrastive learning scheme, implemented through InfoNCE [37], to make the new word \(\langle R\rangle\) representing a specific relation concept converge to the preposition space, thereby capturing interactions between entities rather than just appearance. Furthermore, it emphasizes high-level semantics between entities over low-level appearance features (such as texture or color) through relation-focal importance sampling. Extensive experiments have verified ReVersion's superiority over existing methods in a range of visual relations, demonstrating its significant advantages in visual concept embedding.