ELBO-T2IAlign: A Generic ELBO-Based Method for Calibrating Pixel-level Text-Image Alignment in Diffusion Models
arXiv:2506.09740v2 Announce Type: replace-cross Abstract: Diffusion models excel at image generation. Recent studies have shown that these models not only generate high-quality images but also encode text-image alignment information through attention maps or loss functions. This information is valuable for various downstream tasks, including segmentation, text-guided image editing, and compositional image generation. However, current methods heavily rely on the assumption of perfect text-image alignment in diffusion models, which is not the case. In this paper, we propose using zero-shot refer
This research addresses a known limitation in current diffusion models regarding text-image alignment, which is crucial for their reliable application in various tasks.
Improving the calibration of pixel-level text-image alignment is vital for advancing the reliability and utility of AI in sensitive applications like segmentation and editing, broadening their commercial and industrial adoption.
By proposing an ELBO-based method, this research offers a generic approach to improve the accuracy and robustness of diffusion models, reducing previous reliance on imperfect assumptions.
- · AI developers
- · Creative industries using AI tools
- · Computer vision researchers
- · Generative AI platforms
- · Methods relying on uncalibrated text-image alignment
- · Competitors with less precise generative models
Diffusion models will become more reliable for tasks requiring precise pixel-level control based on text prompts.
This improved reliability will accelerate the development and adoption of AI-driven image editing and content generation tools across industries.
Enhanced precision in generative AI could lead to new applications in fields that demand high accuracy, potentially democratizing advanced visual content creation and analysis.
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Read at arXiv cs.AI