Infusion: Mitigating Overfitting in Customized Text-to-Image Diffusion Models

Kernkonzepte

Infusion enables the learning of target concepts while preserving the generative capacity and modality diversity of the original text-to-image diffusion model, effectively mitigating both concept-agnostic and concept-specific overfitting.

Zusammenfassung

The paper analyzes two types of overfitting in customized text-to-image (T2I) diffusion models: concept-agnostic overfitting and concept-specific overfitting.

Concept-agnostic overfitting undermines the non-customized generative capabilities of the foundational T2I model, while concept-specific overfitting confines the customized model to limited training modalities. To quantify these effects, the authors introduce the "Latent Fisher divergence" and "Wasserstein metric" respectively.

To address these challenges, the authors propose Infusion, a T2I customization method that decouples the attention maps and value features in the cross-attention module. Infusion leverages the attention maps from the foundational T2I model to preserve its generative diversity, while learning a lightweight residual embedding to inject the customized concepts. This approach enables Infusion to achieve plug-and-play single-concept and multi-concept generation, outperforming state-of-the-art methods in both text alignment and conceptual fidelity.

Extensive experiments demonstrate Infusion's robust resistance to overfitting, maintaining high-quality customized generation even with prolonged training on limited data. The authors also conduct a user study, which confirms the general preference for Infusion's customization capabilities over other baselines.

Statistiken

Infusion requires only 11KB of trained parameters for customization, enabling seamless integration and flexible switching between customized and regular generation modes.
Infusion outperforms state-of-the-art methods in text alignment score, achieving up to 86.90% user preference.

Zitate

"Infusion adeptly balances textual expression and concept fidelity."
"Remarkably, Infusion achieves this feat with remarkable efficiency, requiring a mere 11KB of trained parameters."

Wichtige Erkenntnisse aus

Infusion: Preventing Customized Text-to-Image Diffusion from Overfitting

by Weili Zeng,Y... um arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14007.pdf

Tiefere Fragen

How can Infusion's approach be extended to handle more complex and diverse customization scenarios, such as multi-modal inputs or open-ended text prompts

Infusion's approach can be extended to handle more complex and diverse customization scenarios by incorporating multi-modal inputs and open-ended text prompts. For multi-modal inputs, the model can be modified to accept a combination of different types of data, such as images, text, and audio. By integrating multiple modalities, the model can generate more diverse and contextually rich outputs. Additionally, for open-ended text prompts, the model can be trained to understand and interpret a wider range of textual descriptions, allowing for more creative and varied image generation. This extension would involve enhancing the model's language understanding capabilities and incorporating mechanisms to handle ambiguous or abstract prompts effectively.

What are the potential limitations of Infusion's decoupling of attention maps and value features, and how could these be addressed to further improve the model's performance

The decoupling of attention maps and value features in Infusion may have limitations in scenarios where intricate details or fine-grained features are crucial for accurate customization. One potential limitation is the risk of losing fine details or subtle nuances in the generated images when the attention maps and value features are decoupled. To address this, the model could be enhanced by incorporating mechanisms for better coordination between attention and value features, ensuring that both aspects work in harmony to capture and preserve detailed information during customization. Additionally, introducing additional regularization techniques or constraints to maintain the balance between attention and value features could help improve the model's performance in capturing intricate details.

Given the importance of preserving the original model's generative capabilities, how could Infusion's principles be applied to other types of generative models beyond text-to-image diffusion

The principles of preserving the original model's generative capabilities, as demonstrated in Infusion, can be applied to other types of generative models beyond text-to-image diffusion. For instance, in the context of image generation, these principles could be adapted for style transfer models, where the goal is to preserve the style of a reference image while applying it to a different content image. By decoupling the style and content representations in the model and ensuring a balance between the two during generation, similar to Infusion's approach, one can achieve more effective and faithful style transfer results. This concept can also be extended to other generative tasks such as video synthesis or music generation, where maintaining the original model's capabilities while incorporating customized inputs is essential for producing high-quality outputs.