T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models (2302.08453v2)

Abstract: The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meaningful semantics. However, relying solely on text prompts cannot fully take advantage of the knowledge learned by the model, especially when flexible and accurate controlling (e.g., color and structure) is needed. In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly. Specifically, we propose to learn simple and lightweight T2I-Adapters to align internal knowledge in T2I models with external control signals, while freezing the original large T2I models. In this way, we can train various adapters according to different conditions, achieving rich control and editing effects in the color and structure of the generation results. Further, the proposed T2I-Adapters have attractive properties of practical value, such as composability and generalization ability. Extensive experiments demonstrate that our T2I-Adapter has promising generation quality and a wide range of applications.

T2I-Adapter: Enhancing Control in Text-to-Image Diffusion Models

The paper "T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models" addresses a notable limitation in the current state of text-to-image (T2I) diffusion models. While these models demonstrate remarkable generative capabilities, the reliance on text prompts alone results in insufficient control over specific attributes such as color and structure. The research proposes the use of "T2I-Adapters," lightweight, trainable components that align the internal knowledge of large T2I models, such as Stable Diffusion (SD), with external control signals.

Methodology

The authors introduce T2I-Adapters to serve as mediators that connect predefined conditions with the implicit knowledge stored in T2I models. These adapters do not require the retraining of the entire T2I model but instead facilitate the alignment of external guides (e.g., sketches, color palettes) with the model's capabilities. The adapters are designed for plug-and-play functionality, preserving the original model's architecture and performance while enhancing its controllability. Importantly, the adapters are optimized using training datasets without perturbing the pre-existing model weights, and they accommodate a variety of conditions such as spatial color control and intricate structural formations.

Experimental Results

Quantitative results indicate that the T2I-Adapters achieve superior performance in both FID and CLIP Score metrics compared to existing GAN-based and diffusion-based frameworks. The experimental evaluations demonstrate that the T2I-Adapters deliver promising control capabilities while maintaining high generative quality. Notably, the paper indicates the composability of these adapters, showing that multiple adapters can be integrated to simultaneously manage multiple conditions like depth and keypose, yielding complex and semantically accurate images.

Implications and Impact

From a practical standpoint, the proposed T2I-Adapters amplify the utility of T2I models by embedding control features that are essential for tailored image generation applications ranging from artistic content creation to detailed image editing. The adapters' generalizability offers another avenue for expansive application, as they can be directly used across models derived from the same base T2I model. The lightweight nature of these adapters ensures minimal computational overhead, making them accessible tools for diverse computational environments.

Theoretically, this research extends the current understanding of how internal representations within deep generative models can be enhanced via modular, trainable components, broadening the horizon for future exploration into adaptable and efficient model architectures.

Future Developments

Prospective research directions could involve exploring adaptive fusion methods for multi-modal guidance integration, reducing the manual efforts needed for adapter combination. Additionally, further investigation into the scalability of the approach in more diverse and large-scale datasets will be crucial in establishing broader applicability and robustness.

The paper presents a compelling approach to augment the controllability of T2I diffusion models, serving as both a significant contribution and a foundation for future exploration in the domain of controlled generative modeling.