Margin-aware Preference Optimization for Aligning Diffusion Models without Reference

We propose MaPO, a reference-free, sample-efficient, memory-friendly alignment technique for text-to-image diffusion models. For more details on the technique, please refer to our paper here.

  Developed by

Jiwoo Hong* (KAIST AI) Sayak Paul* (Hugging Face) Noah Lee (KAIST AI) Kashif Rasul (Hugging Face) James Thorne (KAIST AI) Jongheon Jeong (Korea University)

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/mapo-t2i/pick-style-cartoon.

The AI portrait generator market is experiencing rapid growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of AI technologies in creative fields, the increasing accessibility of user-friendly AI tools, and the growing need for cost-effective and efficient portrait generation solutions. Applications span from artistic expression and entertainment to marketing and e-commerce, with realistic, artistic, cartoon, and avatar generators catering to specific needs. The market is segmented by application (art, advertising, gaming, fashion, education, social media, others) and type of generator (realistic, artistic, cartoon/anime, avatar/character, others). While precise market sizing data is not provided, considering the rapid growth of AI-powered tools and the prevalence of listed companies, a conservative estimate for the 2025 market size could be placed between $500 million and $1 billion. Considering a plausible CAGR of 25% (a reasonable estimate based on similar rapidly evolving tech markets), this projects significant expansion over the forecast period (2025-2033). North America and Asia-Pacific are expected to hold dominant market shares due to early adoption of AI technologies and substantial technological investments in these regions. However, other regions are showing increasing interest, indicating promising opportunities for market expansion globally. The main restraints are concerns over the ethical implications of AI-generated content, including potential biases and copyright issues, and the need for continuous technological advancements to enhance the quality and realism of generated portraits.
Further growth will depend on addressing these challenges. This includes developing robust ethical guidelines, improving the quality and diversity of generated portraits to reduce biases, and ensuring user privacy and data security. Ongoing innovation in AI algorithms, improved accessibility of powerful hardware, and increased integration with existing creative software platforms will all contribute to market expansion. The proliferation of mobile applications and the increasing ease of use will democratize access, broadening the user base and contributing to market growth. The increasing sophistication of the AI algorithms driving these tools is likely to result in greater realism, enhanced creative control, and a wider range of styles to meet diverse needs. The market's future trajectory will be shaped by these factors, promising significant opportunities for innovation and market penetration in the coming years.

Description:

👉 Download the dataset here

The Anime vs Cartoon Dataset is a specialized collection of images designed to differentiate between two popular forms of animation: anime and cartoons. While both anime and cartoons share similarities in being animated visual media, they possess distinct stylistic and structural characteristics that set them apart. This dataset is intended for use in developing and testing convolutional neural networks (CNNs) or other machine learning models for classification tasks, enabling the automated identification of whether an image belongs to the anime or cartoon category.

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Context and Motivation

Anime and cartoons are both beloved forms of entertainment worldwide, yet they originate from different cultural contexts and exhibit unique artistic styles. Anime, typically associated with Japanese culture, often features detailed backgrounds, unique facial expressions, and a wide range of color palettes, while cartoons, more commonly linked to Western culture, tend to have simpler designs, more exaggerated features, and distinct outlines. These differences, though subtle, provide a rich ground for developing image classification models that can automatically distinguish between the two.

Dataset Content

The dataset consists of images categorized into two distinct classes: Anime and Cartoon. The images were carefully collected and labeled to ensure a balanced representation of both categories. The images span various styles and themes within each category, including different character designs, backgrounds, and color schemes, providing a diverse set of examples for model training.

Anime: Images classified as anime, representing the distinctive characteristics of Japanese animation.

Cartoon: Images classified as cartoons, showcasing the unique style typical of Western animation.

Applications

This dataset is ideal for

Developing convolutional neural networks (CNNs) for image classification tasks.

Fine-tuning pre-trained models on a new domain-specific dataset.

Exploring the stylistic differences between anime and cartoon animations.

Building automated systems for content moderation or media sorting based on animation type.

This dataset is sourced from Kaggle.

Description:

👉 Download the dataset here

The CartoonSet10K is a diverse and expansive collection of 10,000 unique 2D cartoon avatar images, designed to serve as a resource for various applications such as image recognition, generative modeling, and machine learning projects. Each avatar in the dataset is randomly generated by combining various distinct features, allowing for immense variety and creative exploration.

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Dataset Structure:

The dataset contains avatars that are systematically varied across multiple design categories, ensuring a wide range of visual diversity. Specifically, the dataset features variability in:

10 Artwork Categories: These categories encompass various avatar elements, such as facial shapes, hairstyles, eyes, mouths, noses, and other key facial features, all designed in a consistent cartoon style.

4 Color Categories: The avatars exhibit diversity in color combinations that influence different aspects of the character design, including skin tones, hair colors, eye colors, and accessory colors, making the dataset highly versatile for tasks that require color differentiation.

4 Proportion Categories: These categories control the proportions of various facial features, allowing for the exploration of character diversity in terms of size and scaling of facial elements such as eyes, nose, mouth, and head shapes.

Dataset Utility:

This dataset is designed to offer a nearly infinite variety of combinations, with approximately 10^13 possible unique avatars that could theoretically be generated using this dataset's variables. This rich variety makes Cartoon Set10K ideal for.

Generative Adversarial Networks (GANs): Researchers and developers can use the dataset to train models that can generate new avatars or variations based on learned patterns.

Image Classification: The dataset can be employed for classifying different styles, facial features, and color schemes, providing a robust platform for image recognition and feature extraction tasks.

Data Augmentation: Cartoon Set10K offers an excellent opportunity for data augmentation in training image-based Al models, given its immense variability and combinatorial potential.

This dataset is sourced from Kaggle.