UltraEdit: Instruction-based Fine-Grained Image Editing at Scale (2407.05282v2)

Abstract: This paper presents UltraEdit, a large-scale (approximately 4 million editing samples), automatically generated dataset for instruction-based image editing. Our key idea is to address the drawbacks in existing image editing datasets like InstructPix2Pix and MagicBrush, and provide a systematic approach to producing massive and high-quality image editing samples. UltraEdit offers several distinct advantages: 1) It features a broader range of editing instructions by leveraging the creativity of LLMs alongside in-context editing examples from human raters; 2) Its data sources are based on real images, including photographs and artworks, which provide greater diversity and reduced bias compared to datasets solely generated by text-to-image models; 3) It also supports region-based editing, enhanced by high-quality, automatically produced region annotations. Our experiments show that canonical diffusion-based editing baselines trained on UltraEdit set new records on MagicBrush and Emu-Edit benchmarks. Our analysis further confirms the crucial role of real image anchors and region-based editing data. The dataset, code, and models can be found in https://ultra-editing.github.io.

Overview of the ULTRA EDIT Dataset for Instruction-based Image Editing

The paper introduces a comprehensive dataset known as ULTRA EDIT, designed to enhance instruction-based image editing at a large scale. Despite existing precedents like InstructPix2Pix and MagicBrush, ULTRA EDIT is posited as an advancement addressing several critical gaps present in these earlier datasets. The dataset is substantial, comprising approximately 4 million editing samples with around 750,000 unique instructions. The authors' approach draws on the creative input of LLMs, augmented by human-written contextual examples, to produce a diverse and robust set of editing instructions.

Key Advantages and Methodologies

The ULTRA EDIT dataset distinguishes itself through several notable features:

1. Diversity in Instructions: The dataset benefits from the integration of LLMs for scalable instruction generation, offering a broad spectrum of editing scenarios. This overcomes the scalability challenges faced by purely human-annotated datasets and the limited scope of LLM-enabled datasets.
2. Foundation on Real Images: To mitigate the biases and limitations of text-to-image (T2I) models commonly employed in previous datasets, ULTRA EDIT anchors its samples in real images. This foundation enhances the diversity and reduces potential biases that might skew the dataset if generated solely through model-based synthesis.
3. Support for Region-based Editing: Beyond free-form editing, ULTRA EDIT incorporates region-based editing data. This involves specifying regions within images, which boosts performance in tasks requiring precise manipulations, thus filling a notable gap in many existing datasets.

Implications and Contributions

The dataset holds significant implications for and contributions to the domain of image editing using AI models:

• Performance Gains: The application of ULTRA EDIT in training canonical diffusion-based editing models has led to new records, particularly on the MagicBrush and Emu-Edit benchmarks. This demonstrates the dataset’s potential to improve instruction adherence and visual fidelity in generated edits.
• Enhanced Precision in Region-based Edits: Empirical results suggest that the inclusion of region-based data remarkably enhances the precision of models in tasks requiring localized changes.
• Robustness and Scalability: The dataset addresses the need for scalable yet comprehensive datasets that can support the development of more robust image editing models.

Future Perspectives and Developments

The paper’s findings open several avenues for future research and development:

• Expansion of Region-based Data: Considering the substantial benefits observed, a future research direction could involve expanding the scale and diversity of region-based editing instructions.
• Bootstrapped Training Techniques: Future work might explore the development of bootstrapped training methodologies, leveraging ULTRA EDIT to iteratively refine and enhance model performance.
• Addressing Remaining Biases: While the dataset reduces biases by anchoring on real images, refining techniques to further mitigate any residual biases would be beneficial.

In summary, the ULTRA EDIT dataset represents a substantial advancement in the field of instruction-based image editing. It provides a scalable, diverse, and robust framework for improving the performance and precision of image editing models. The methodologies and insights presented in this paper set a foundational benchmark for future datasets in this context and imply ongoing opportunities for refinement and application in both academic and industrial settings.