Customizing Text-to-Image Models with a Single Image Pair to Learn Artistic Styles

Konsep Inti

A new customization method that learns stylistic difference from a single image pair and applies the acquired style to the generation process, enabling text-to-image models to generate stylized images while preserving the original content structure.

Abstrak

The paper introduces a new task of customizing text-to-image models using a single image pair that demonstrates a distinct artistic style. The authors propose a method called "Pair Customization" that learns the stylistic difference between the paired images and applies the acquired style to the generation process.
The key highlights are:

Existing customization methods struggle to capture the artistic style accurately and often overfit to the specific image content in the examples.
The authors leverage the contrast between the image pair to generate pairwise consistent images while better disentangling style and content.
They employ a joint optimization method with separate sets of low-rank adaptation (LoRA) weights for style and content, encouraging orthogonality between them.
A new "style guidance" is introduced during inference to better preserve the original image structure when applying the learned style.
Experiments on various image pairs demonstrate the advantage of the proposed method in preserving diverse structures while applying the stylization faithfully, compared to existing customization baselines.

Statistik

The authors construct a diverse dataset of 40 content images for each class (headshots, animals, landscapes) and apply image editing/translation techniques to obtain the stylized version.
They use LEDITS++, white-box cartoonization, Stylized Neural Painting, and posterization to create the synthetic image pairs.

Kutipan

"Our method accurately follows the given style, turning the background into a single color matching the original background and preserving the identity and pose for each dog."
"Unlike existing methods that learn to mimic a single concept from a collection of images, our method captures the stylistic difference between paired images. This allows us to apply a stylistic change without overfitting to the specific image content in the examples."

Wawasan Utama Disaring Dari

Customizing Text-to-Image Models with a Single Image Pair

by Maxwell Jone... pada arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.01536.pdf

Pertanyaan yang Lebih Dalam

How can the proposed method be extended to handle a larger diversity of styles beyond the synthetic pairs used in the experiments?

The proposed method can be extended to handle a larger diversity of styles by incorporating a more extensive and varied dataset of image pairs during training. Instead of relying solely on synthetic pairs, real-world image pairs showcasing a wide range of artistic styles can be included. This would provide the model with a richer and more diverse set of examples to learn from, enabling it to generalize better to unseen styles. Additionally, leveraging techniques such as data augmentation and style augmentation can help introduce more variability in the training data, allowing the model to learn to adapt to a broader spectrum of styles. Furthermore, incorporating transfer learning from pre-trained models that have been exposed to a diverse range of styles can also enhance the model's ability to handle a larger diversity of styles effectively.

What are the potential limitations of the disentanglement approach, and how could it be further improved to handle more complex style-content relationships?

One potential limitation of the disentanglement approach is the challenge of accurately separating style and content in images that exhibit intricate and intertwined relationships between the two. In more complex style-content relationships, where the style heavily influences the content and vice versa, disentangling them effectively can be challenging. To address this limitation and improve the handling of complex style-content relationships, advanced disentanglement techniques such as adversarial training or incorporating additional constraints during training can be employed. These techniques can help the model learn more robust representations of style and content, making it easier to disentangle them accurately. Additionally, exploring hierarchical disentanglement approaches that capture multiple levels of abstraction in style and content representation can enhance the model's ability to handle complex relationships between style and content.

Given the computational cost of the test-time optimization, how could the method be adapted to enable faster, more efficient customization of text-to-image models?

To enable faster and more efficient customization of text-to-image models, the method could be adapted in several ways:

Encoder-Based Approaches: Implementing encoder-based approaches for predicting style and content weights in a feed-forward manner can significantly speed up the customization process. By leveraging pre-trained encoders to predict the necessary adjustments for style and content, the model can quickly adapt to new styles without the need for extensive test-time optimization.

Parallel Processing: Utilizing parallel processing techniques and distributed computing resources can help accelerate the customization process by enabling multiple instances of the model to run simultaneously. This can reduce the overall time required for customization tasks.

Incremental Learning: Implementing incremental learning strategies where the model is updated gradually with new style-content pairs over time can help distribute the computational load and make the customization process more efficient. By updating the model iteratively with new data, it can adapt to new styles without the need for extensive retraining from scratch.

Model Optimization: Optimizing the model architecture and training process for efficiency, such as using lightweight architectures or optimizing hyperparameters, can also contribute to faster customization of text-to-image models. By streamlining the model's structure and training procedures, the computational cost can be reduced while maintaining performance.