HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing (2404.09990v1)

Abstract: This study introduces HQ-Edit, a high-quality instruction-based image editing dataset with around 200,000 edits. Unlike prior approaches relying on attribute guidance or human feedback on building datasets, we devise a scalable data collection pipeline leveraging advanced foundation models, namely GPT-4V and DALL-E 3. To ensure its high quality, diverse examples are first collected online, expanded, and then used to create high-quality diptychs featuring input and output images with detailed text prompts, followed by precise alignment ensured through post-processing. In addition, we propose two evaluation metrics, Alignment and Coherence, to quantitatively assess the quality of image edit pairs using GPT-4V. HQ-Edits high-resolution images, rich in detail and accompanied by comprehensive editing prompts, substantially enhance the capabilities of existing image editing models. For example, an HQ-Edit finetuned InstructPix2Pix can attain state-of-the-art image editing performance, even surpassing those models fine-tuned with human-annotated data. The project page is https://thefllood.github.io/HQEdit_web.

• The paper introduces HQ-Edit, a novel dataset of 200,000 instruction-based image edits that significantly improves alignment and coherence metrics.
• It details a self-instruct inspired pipeline leveraging GPT-4V and DALL-E 3 to generate high-fidelity diptychs with precise text prompt refinement.
• The new metrics, Alignment and Coherence, validate HQ-Edit’s effectiveness, establishing a new benchmark for training advanced image editing models.

HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing

This paper introduces HQ-Edit, a large-scale, high-quality dataset explicitly designed for instruction-based image editing, comprising approximately 200,000 edits. This dataset is curated through a synergistic combination of state-of-the-art foundational models, GPT-4V and DALL-E 3, which collectively elevate the resolution and fidelity of image-editing tasks beyond the capabilities of previous methodologies.

Key Contributions

1. Data Collection Pipeline:
   • Expansive Data Generation: The initial dataset is expanded using a pipeline inspired by Self-instruct, where seed triplets consisting of input images, output images, and corresponding edit instructions from online sources are scaled up to around 100,000 instances.
   • Diptych Generation: GPT-4 formats these triplets into detailed prompts for DALL-E 3, generating diptychs that exhibit superior alignment and consistency.
   • Post-processing: Sequential refinement of the generated diptychs and their texts ensures high alignment between image pairs and corresponding instructions. This includes image decomposition, warping, and filtering for precision, along with text instruction enhancements using GPT-4V.
2. Evaluation Metrics: The paper introduces two novel quantitative metrics, Alignment and Coherence, to rigorously assess the quality of image edits.
   • Alignment measures whether the image modifications adhere closely to the provided instructions while maintaining the integrity of non-modified areas.
   • Coherence evaluates the overall aesthetic quality of the edited images, ensuring consistency in lighting, shadows, style, and edge definition.

Experimental Results

Through empirical validation, the authors demonstrate the efficacy of HQ-Edit in training advanced image editing models:

• An InstructPix2Pix model fine-tuned on HQ-Edit achieves notable improvements with a 12.3 increase in Alignment and a 5.64 enhancement in Coherence over its baseline version.
• When benchmarked against existing datasets like InstructPix2Pix, HIVE, and MagicBrush, HQ-Edit outperforms them significantly in both Alignment and Coherence metrics.

Implications and Future Developments

Practical Implications:

• Application Enhancement: HQ-Edit significantly enriches image editing applications across various domains such as digital art, advertising, and film production, allowing for more precise and aesthetically pleasing edits guided by detailed instructions.
• Tool Development: The availability of HQ-Edit provides a robust foundation for developing new editing tools that can process complex, instruction-driven modifications in images.

Theoretical Implications:

• Dataset Quality: HQ-Edit’s high-resolution images and detailed prompts set a new benchmark for datasets in generative models, underscoring the significance of alignment and coherence in training more reliable models.
• Model Training: This dataset promotes the development of models that not only generate high-quality images but also maintain strict adherence to text-based instructions, pushing the frontier in multi-modal AI research.

Speculative Future Developments:

• Enhanced Models: Building upon HQ-Edit, future models could explore utilizing other emerging foundational models to further enhance the edit quality and diversity. Moreover, integrating real-time feedback mechanisms could refine the editing process dynamically.
• Cross-Modal Interactions: Expanding the HQ-Edit methodology to incorporate other forms of data, such as audio or video, could pioneer new research avenues in cross-modal and temporal editing.

Conclusion

HQ-Edit represents a substantial advancement in instruction-based image editing by leveraging advanced models like GPT-4V and DALL-E 3 to provide a high-quality, expansive, and meticulously refined dataset. The introduction of innovative metrics further consolidates its value in propelling future research and development in generative models and automated image editing.

The paper underscores the practical and theoretical implications of HQ-Edit, presenting a compelling case for its adoption in training more accurate and coherent image editing models. Future research, bolstered by HQ-Edit, is poised to explore new methodologies and applications that transcend current generative model capabilities.