Gen4Gen: Generative Data Pipeline for Generative Multi-Concept Composition
(2402.15504)Abstract
Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examples for training. This paper tackles two interconnected issues within this realm of personalizing text-to-image diffusion models. First, current personalization techniques fail to reliably extend to multiple concepts -- we hypothesize this to be due to the mismatch between complex scenes and simple text descriptions in the pre-training dataset (e.g., LAION). Second, given an image containing multiple personalized concepts, there lacks a holistic metric that evaluates performance on not just the degree of resemblance of personalized concepts, but also whether all concepts are present in the image and whether the image accurately reflects the overall text description. To address these issues, we introduce Gen4Gen, a semi-automated dataset creation pipeline utilizing generative models to combine personalized concepts into complex compositions along with text-descriptions. Using this, we create a dataset called MyCanvas, that can be used to benchmark the task of multi-concept personalization. In addition, we design a comprehensive metric comprising two scores (CP-CLIP and TI-CLIP) for better quantifying the performance of multi-concept, personalized text-to-image diffusion methods. We provide a simple baseline built on top of Custom Diffusion with empirical prompting strategies for future researchers to evaluate on MyCanvas. We show that by improving data quality and prompting strategies, we can significantly increase multi-concept personalized image generation quality, without requiring any modifications to model architecture or training algorithms.
Overview
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The paper introduces Gen4Gen, a semi-automated pipeline for creating datasets, and MyCanvas, a dataset aimed at improving multi-concept personalization in text-to-image generation.
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Gen4Gen utilizes advancements in foundation models to generate realistic images with detailed text descriptions, aimed at addressing the shortcomings of current datasets in handling complex multi-concept scenarios.
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A novel evaluation metric, consisting of CP-CLIP and TI-CLIP scores, is proposed to quantitatively assess the capability of models in generating personalized images that accurately align with textual descriptions.
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Empirical tests demonstrate the effectiveness of using the MyCanvas dataset to enhance the performance of existing diffusion models, suggesting a significant improvement in generating realistic, multi-concept personalized images.
Enhancing Multi-Concept Personalization in Text-to-Image Generation with Gen4Gen
Introduction
The evolution of text-to-image diffusion models has unlocked new doors in the creation of personalized images, combining multiple user-defined concepts into a single coherent scene. Despite remarkable advancements, perfecting multi-concept personalization remains a significant challenge. Traditional personalization methods struggle with complex scene compositions, often due to a mismatch between simplistic text descriptions and the desired intricate visual outputs. Addressing these challenges, this paper introduces Gen4Gen, a semi-automated dataset creation pipeline, and MyCanvas, a dataset designed for benchmarking multi-concept personalization. Furthermore, a novel evaluation metric comprising CP-CLIP and TI-CLIP scores is proposed to quantitatively assess the models' capability in generating personalized multi-concept images.
MyCanvas: Proposing a New Benchmark for Personalized Text-to-Image Generation
MyCanvas emerges as a response to the inadequacies of current datasets in accommodating the intricacies of multi-concept personalization. Leveraging advancements in foundation models, Gen4Gen synthesizes realistic, custom images with corresponding densely detailed text descriptions. This dataset not only improves upon the existing datasets' quality but also introduces more challenging scenarios for text-to-image models by including images with multiple, semantically similar objects in complex compositions.
Dataset Design Principles
The design of MyCanvas is guided by three principles:
- Detailed Text-Image Alignment: Every image is paired with a comprehensive text description, ensuring a precise match between the visual content and the textual narrative.
- Logical Object Layout with Reasonable Backgrounds: The pipeline ensures realistic object coexistence and positioning, providing images that surpass the simplistic 'cut-and-paste' appearance of traditional datasets.
- High Resolution: Maintaining high resolution is pivotal to support the generation of detailed, high-quality personalized images.
Gen4Gen Pipeline
Gen4Gen streamlines the creation of the MyCanvas dataset through a three-stage process:
- Object Association and Foreground Segmentation: Combining objects likely to appear in real-world scenes, applying object segmentation to generate foregrounds.
- LLM-Guided Object Composition: Utilizing LLMs to dictate the composition layout and background scenario suggestions.
- Background Repainting and Image Recaptioning: Enhancing foreground objects with suitable backgrounds followed by detailed recaptioning to ensure text-image alignment.
Novel Evaluation Metrics: CP-CLIP and TI-CLIP
Evaluating the effectiveness of text-to-image models in the context of personalized multi-concept images necessitates metrics that can capture both the accuracy of concept representation and the alignment with textual descriptions. The CP-CLIP score evaluates how well a model generates images that incorporate all personalized concepts with high fidelity, whereas the TI-CLIP score measures the alignment between the generated image and the entire text description, serving as a means to detect potential overfitting to training backgrounds.
Empirical Results and Findings
Empirical tests on the MyCanvas dataset reveal significant improvements in generating realistic multi-concept images using existing diffusion models with enhanced data quality and prompting strategies. Specifically, the study outlines how Custom Diffusion benefits from the quality and complexity of the MyCanvas dataset, achieving a notable boost in the generation of personalized images as measured by the CP-CLIP and TI-CLIP scores.
Future Directions and Conclusion
This research underscores the critical role of high-quality datasets and innovative evaluation metrics in advancing personalized text-to-image generation. As AI models continue to evolve, the integration of foundation models into dataset creation processes like Gen4Gen offers promising avenues for crafting tailored datasets that address specific challenges within computer vision tasks. The introduction of MyCanvas sets a new benchmark for evaluating and improving multi-concept personalization in generative models, potentially stimulating further research in dataset quality enhancement and model evaluation methodologies.
This paper represents a significant stride towards understanding and perfecting the generation of personalized, multi-concept text-to-image models. Through the Gen4Gen pipeline, the research community gains access to a sophisticated tool for developing datasets that better align with the nuanced requirements of personalized image generation. As we look to the future, it is anticipated that these innovations will not only refine the capabilities of generative models but also unlock new potentials in creating deeply personalized and contextually rich visual content.
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