IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models (2308.06721v1)

Abstract: Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tricky to generate desired images using only text prompt as it often involves complex prompt engineering. An alternative to text prompt is image prompt, as the saying goes: "an image is worth a thousand words". Although existing methods of direct fine-tuning from pretrained models are effective, they require large computing resources and are not compatible with other base models, text prompt, and structural controls. In this paper, we present IP-Adapter, an effective and lightweight adapter to achieve image prompt capability for the pretrained text-to-image diffusion models. The key design of our IP-Adapter is decoupled cross-attention mechanism that separates cross-attention layers for text features and image features. Despite the simplicity of our method, an IP-Adapter with only 22M parameters can achieve comparable or even better performance to a fully fine-tuned image prompt model. As we freeze the pretrained diffusion model, the proposed IP-Adapter can be generalized not only to other custom models fine-tuned from the same base model, but also to controllable generation using existing controllable tools. With the benefit of the decoupled cross-attention strategy, the image prompt can also work well with the text prompt to achieve multimodal image generation. The project page is available at \url{https://ip-adapter.github.io}.

Introduction

The landscape of image generation driven by text-to-image diffusion models has substantially expanded, with prominent examples such as GLIDE, DALL-E 2 and Imagen demonstrating remarkable capabilities. However, these models often necessitate intricate text prompting techniques to achieve the desired results, thus presenting challenges in terms of expressivity and resource demands. Simultaneously, image prompts bring forth an alternative—offering rich content representation, but with limitations in terms of model compatibility and the need for extensive computational resources for fine-tuning.

Approach and Methodology

Against this backdrop, the paper presents IP-Adapter—a novel approach designed to furnish pre-existing text-to-image diffusion models with image prompt capabilities, while maintaining compatibility with conventional text prompts. At the heart of the IP-Adapter is the decoupled cross-attention mechanism, which distinctly processes text and image features through separate cross-attention layers within the generative model’s architecture. The method ensures the pre-trained diffusion model remains undisturbed, enabling seamless generalization across various custom models originally derived from the same foundational diffusion model. Moreover, it allows for the blending of image and text prompts, thereby enriching the multimodal generative landscape.

Results and Contributions

Quantitative and qualitative assessments of IP-Adapter underscore its proficiency, showing on-par or superior performance against existing fine-tuned models with a fraction of trainable parameters (~22M). The adapter's decoupled design not only fosters compatibility with text prompts for multimodal generation, but also aligns seamlessly with extant controllable tools such as ControlNet. Moreover, it exhibits flexibility across different styles and structures when integrated with adapted community models, effectively demonstrating its versatility and numerous applications in advanced image generation tasks.

Conclusion

The paper's proposed IP-Adapter stands out as a pivotal development for leveraging image prompts in text-to-image diffusion models, striking a balance between expressive power and computational efficiency. It provides a scalable and adaptable solution that circumvents the traditional fine-tuning approach's pitfalls. The use of decoupled cross-attention layers significantly enhances feature integration, thus improving the fidelity of generated images. Moving forward, the authors propose further advancements to amplify the consistency of image prompts, aiming to establish even more powerful adaptation methods extending beyond content and style reproduction.