arXiv:2606.06813v1 Announce Type: cross Abstract: Recent text-to-image models built on large-scale Transformer backbones and flow-based objectives deliver strong text-image alignment and high visual quality, yet often produce overly similar samples under a fixed prompt. Existing diversity-enhancement methods alleviate this issue, but typically require expensive sampling or auxiliary optimization, incurring non-trivial overhead. To investigate the root cause of this homogeneity, we examine intermediate Transformer features and observe that the zero-frequency spatial average (DC) component rapid
The proliferation of advanced text-to-image models has revealed a critical limitation in sample diversity, prompting immediate investigation into underlying model architectures to address this bottleneck.
Improving diversity in generative AI outputs is crucial for broader applicability, preventing algorithmic monoculture, and enhancing user experience across creative industries and research.
New methods focused on representation modulation offer a more efficient way to achieve diverse AI-generated imagery without significant computational overhead, diverging from current expensive sampling or optimization techniques.
- · AI model developers
- · Creative industries relying on generative AI
- · Users of text-to-image platforms
- · Generative AI research community
- · Platforms with undifferentiated image generation
- · Computational resource providers tied to inefficient diversity methods
More diverse and creative outputs from text-to-image models become readily accessible.
The improved efficiency in generating diverse content could accelerate the adoption of generative AI in new applications and reduce operational costs.
Enhanced diversity could mitigate biases embedded in current models by offering a wider range of visual representations, influencing societal perceptions over time.
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Read at arXiv cs.AI