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T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models

783
Citations
March 24, 2024
Published Date

Research Abstract & Technology Focus

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., structure and color) 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 low-cost 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. Our code is available at https://github.com/TencentARC/T2I-Adapter.
i-adapter learning adapters controllable ability text-to-image
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Frequently Asked Questions (FAQ)

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What is the core focus of the research titled 'T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models'?

This literature focuses on: 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...

Are there open-source GitHub repositories related to T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models?

Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.

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What other academic literature is closely related to 'T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models'?

Yes, highly correlated activity was mapped. An entry titled 'T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models' discusses this: The incredible generative ability of large-scale text-to-image (T2I) models has demonstrated strong power of learning complex structures and meanin...

Are there commercial applications of 'T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models' in market news publications?

Yes, highly correlated activity was mapped. An entry titled 'Disentangled autoencoding equivariant diffusion model for controlled generation of 3D molecules' discusses this: Controlled generation of 3D molecules with desired properties can speed up drug discovery. Here, the authors propose a semantics-guided diffusion m...

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