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CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models (2312.06059v1)

Published 11 Dec 2023 in cs.CV, cs.AI, and cs.LG

Abstract: Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt, where the model might overlook or entirely fail to produce certain objects. Existing solutions often require customly tailored functions for each of these problems, leading to sub-optimal results, especially for complex prompts. Our work introduces a novel perspective by tackling this challenge in a contrastive context. Our approach intuitively promotes the segregation of objects in attention maps while also maintaining that pairs of related attributes are kept close to each other. We conduct extensive experiments across a wide variety of scenarios, each involving unique combinations of objects, attributes, and scenes. These experiments effectively showcase the versatility, efficiency, and flexibility of our method in working with both latent and pixel-based diffusion models, including Stable Diffusion and Imagen. Moreover, we publicly share our source code to facilitate further research.

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Citations (18)
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Summary

  • The paper introduces a training-free contrastive method that improves text-to-image fidelity by accurately associating objects with attributes.
  • It leverages attention maps as features in a contrastive loss to enable model-agnostic enhancements on pre-trained diffusion models.
  • Empirical tests show superior CLIP and TIFA scores along with strong user preference for images that reflect complex prompt semantics.

Introduction

Recognizing objects and attributes accurately from textual descriptions has been a fundamental challenge in text-to-image diffusion models. While recent advancements like Stable Diffusion and Imagen have broken new ground in image generation quality, they often stumble when it comes to faithfully representing the semantic intent of complex text prompts. Drawbacks such as missing objects, misattributed characteristics, and incorrect quantities remain pervasive issues that hinder the reliability of these otherwise impressive generative models.

The industry has approached the problem through various solutions, such as optimizing cross-attention maps to emphasize object presence or employing dual loss functions to clearly define attention areas. While these methods have made strides in improving fidelity, they fall short in handling complex prompts due to the bespoke nature of their objective functions which necessitates sub-optimal, prompt-specific tuning.

Methodology

Our proposal, CONFORM, addresses these limitations within a contrastive framework that intuitively maintains the relationship between objects and their attributes while segregating unrelated elements. By treating attributes of a specific object as positive pairs and contrasting them against unrelated objects or attributes, our method enhances the accuracy and detail of object representation considerably.

CONFORM is a training-free approach leveraging a contrastive objective combined with test-time optimization. This means it can be applied to pre-trained models without additional training requirements, yielding improvements in existing setups. Importantly, the technique is model-agnostic and has been tested extensively on leading models like Stable Diffusion and Imagen.

The core technical innovation lies in our use of attention maps, which we treat as features to train our contrastive loss function. These maps, delineating the interface between input text and generated pixels, guide the generation process to produce images that more faithfully adhere to the given prompt.

Results and Conclusion

Empirical evidence from extensive experiments across various datasets and scenarios demonstrates the efficiency and effectiveness of CONFORM. For instance, when tasked with generating images for complex prompts involving multiple objects and attributes, our method not only produces images with missing objects but also correctly binds attributes to their respective subjects—surpassing other state-of-the-art methods.

In terms of quantitative performance, our approach consistently achieves superior image-text similarity, as determined by CLIP scores, and outperforms competitors in terms of TIFA scores—a metric that evaluates text-to-image fidelity.

A user paper further confirms these findings, with participants overwhelmingly choosing images generated by our method as the most accurate representations of given text prompts. These results reinforce our method's capacity to align closely with semantic intent across various content generation tasks.

In summary, the flexibility and robustness of CONFORM underscore a significant advancement towards addressing fidelity issues in text-to-image models. By publicizing our source code, we invite the research community to build upon and extend the achievements of this work, reinforcing the AI's proficiency in understanding and visualizing complex human language.

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