arXiv:2509.23876v3 Announce Type: replace-cross Abstract: Autoregressive (AR) models based on next-scale prediction have emerged as a powerful tool for image generation, but they face a critical weakness: information inconsistencies between patches across timesteps introduced by progressive resolution scaling. These inconsistencies scatter guidance signals, causing them to drift away from salient regions within the image and leaving behind ambiguous, unfaithful features during sampling. We tackle this challenge with Information-Grounding Guidance (IGG), a novel framework that anchors guidance
The paper addresses current challenges in autoregressive image generation models, a rapidly evolving field, by proposing a novel framework to improve consistency and fidelity.
Improving image generation quality and consistency through better guidance mechanisms is critical for the advancement and broader application of AI in various visual domains.
The proposed Information-Grounding Guidance (IGG) could lead to more robust and higher-quality visual outputs from autoregressive models, reducing artifacts and improving faithfulness.
- · AI model developers
- · Creative industries using AI for visual design
- · Generative AI platforms
- · Platforms with lower-quality image generation APIs
- · Traditional visual content creators unwilling to adapt to AI tools
Higher fidelity and more reliable AI-generated images become more widely accessible.
This could accelerate the adoption of AI in visual content creation across industries like entertainment, advertising, and e-commerce.
The enhanced realism and control in generated imagery might blur the lines between real and synthetic visuals, posing new challenges for content verification.
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Read at arXiv cs.AI