Consistent Character Generation in Text-to-Image Diffusion Models via Cluster-Conditioned Guidance

Konsep Inti

A novel one-shot tuning paradigm, termed as OneActor, that can efficiently generate consistent images of the same character by leveraging the intrinsic cluster structure in the latent space of pre-trained text-to-image diffusion models.

Abstrak

The paper proposes a new cluster-guided paradigm, OneActor, for consistent character generation in text-to-image diffusion models. The key insights are:

The authors derive a cluster-based score function that aims to increase the probability of generating images from the target character cluster and reduce the probability of generating images from auxiliary clusters.

They construct a cluster-conditioned model that introduces the generated samples as the cluster representation and transforms it into a semantic offset to guide the denoising trajectory.

During tuning, the authors devise auxiliary components to simultaneously augment the tuning and regulate the inference, which significantly enhances the content diversity of generated images.

The authors prove that the semantic space of text-to-image diffusion models shares the same interpolation property as the latent space, which can be leveraged for fine generation control.

Comprehensive experiments show that OneActor outperforms a variety of baselines in terms of character consistency, prompt conformity, and image quality, while being at least 4x faster than tuning-based baselines.

Statistik

The paper reports that OneActor takes an average tuning time of 5 minutes, which is at least 4x faster than tuning-based baselines like TheChosenOne (20 minutes on average).

Kutipan

"We argue that a lightweight but intricate guidance is enough to function."
"We first prove that the semantic space has the same interpolation property as the latent space dose. This property can serve as another promising tool for fine generation control."

Wawasan Utama Disaring Dari

OneActor: Consistent Character Generation via Cluster-Conditioned Guidance

by Jiahao Wang,... pada arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10267.pdf

Pertanyaan yang Lebih Dalam

How can the proposed cluster-guided generation paradigm be extended to other conditional image generation tasks beyond character consistency, such as object-centric or scene-centric generation

The proposed cluster-guided generation paradigm can be extended to other conditional image generation tasks beyond character consistency by adapting the concept of cluster-conditioned guidance to different contexts. For object-centric generation, the model can be trained to identify clusters of different object categories and guide the generation process towards a specific object category based on the input prompt. This can ensure consistency in generating objects of the same category across different prompts. Similarly, for scene-centric generation, the model can learn to identify clusters representing different types of scenes or environments and guide the generation process towards a specific scene type based on the input prompt. By incorporating cluster-conditioned guidance in these tasks, the model can generate consistent and contextually relevant images based on the desired category or scene.

What are the potential limitations or failure cases of the semantic interpolation technique, and how can they be addressed

One potential limitation of the semantic interpolation technique is the risk of overfitting to the semantic space, leading to a loss of diversity in the generated images. This can occur when the semantic scale parameter is set too high, causing the model to focus excessively on matching the semantic details of the input prompt and sacrificing variation in the generated images. To address this limitation, it is essential to carefully tune the semantic scale parameter to strike a balance between consistency and diversity. Additionally, incorporating regularization techniques or introducing randomness in the interpolation process can help prevent overfitting and maintain diversity in the generated images.

Can the insights from this work be applied to improve the consistency and controllability of text-to-image generation in other domains, such as generating consistent backgrounds, environments, or scenes

The insights from this work can be applied to improve the consistency and controllability of text-to-image generation in other domains, such as generating consistent backgrounds, environments, or scenes. By leveraging the cluster-guided generation paradigm and semantic interpolation techniques, models can be trained to generate images with consistent background elements or environmental features based on textual descriptions. This approach can ensure that the generated images align closely with the intended scene context and exhibit a high level of consistency in background elements. By incorporating semantic interpolation with appropriate parameter settings, the model can maintain a balance between consistency and diversity, enhancing the overall quality and controllability of text-to-image generation in various domains.