Advancements in Text-to-Image Diffusion Models for Personalized Image Generation: A Review of ID-Preserving Techniques of InstantID

Abstract

The evolution of text-to-image diffusion models, such as GLIDE, DALL-E 2, and Stable Diffusion, has significantly enhanced image generation capabilities. However, achieving image personalization with precise facial detail retention, minimal reference images, and reduced computational costs remains challenging. Traditional methods like DreamBooth and Textual Inversion rely on extensive fine-tuning, while techniques like IP-Adapter, which avoid fine-tuning, often compromise accuracy. Addressing these gaps, InstantID introduces a novel plug-and-play module that uses a single reference image to enable efficient identity preservation with high fidelity and flexibility. 

InstantID departs from conventional approaches by employing ID Embedding and an Image Adapter to enhance semantic richness and facial detail fidelity. Unlike models relying on CLIP-based visual prompts, InstantID integrates ID Embedding with ControlNet to refine the cross-attention process. This involves using simplified facial keypoints for conditional input and replacing text prompts with ID Embedding. Trained on a large-scale dataset comprising LAION-Face and additional high-quality annotated images, InstantID demonstrates superior ID retention and facial detail restoration. Notably, its performance improves with multiple reference images but remains highly effective with just one. 

The results highlight the effectiveness of InstantID's modular components, such as IdentityNet and the Image Adapter, in ensuring exceptional generation quality and detail retention. Although currently optimized for SDXL checkpoints, InstantID offers a scalable and efficient solution for personalized image generation. By integrating with tools like ComfyUI, it provides a seamless and accessible approach to image personalization with strong ID control and adaptability.