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Life sciences · Preprint
arXiv · August 14, 2026
Posted before peer review. The findings may change or fail to hold.
This is an unrefereed preprint introducing Concept Guidance (CoG), a computational method for controlling text-to-image diffusion models without additional training or external models. The work demonstrates technique validation across multiple popular models (PixArt-alpha, SD3, SD3.5, FLUX.1-dev) but lacks peer review, quantitative performance metrics, and head-to-head comparison data in the provided abstract.
Preprint. Intervention: Concept Guidance (CoG): a method that quantifies each layer's concept-specific impact and guides denoising using weighted combinations of predictions with concept-relevant layers skipped..
Concept-dependent differences identified between individual network layers in diffusion models CoG enables concept-specific guidance without additional training, external models, gradients, or prompt engineering Method validated on multiple popular models: PixArt-alpha, SD3, SD3.5, and FLUX.1-dev
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This is an unrefereed arXiv preprint describing a novel computational method for text-to-image generation; it reports technique validation but lacks peer review and clinical or direct translational relevance.
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Text-to-image diffusion models have two major drawbacks that severely limit their practical utility: (1) standard models lack an intrinsic mechanism for continuous, concept-specific guidance (e.g., for precisely controlling how aesthetically pleasing an image looks), and (2) they lack reliability for tasks requiring high local coherence (e.g., generating text or human hands). To tackle these issues, we introduce a novel notion of concept-wise mutual information and find large, concept-dependent differences between individual layers, demonstrating that the generation of specific structures is localized in distinct parts of the network. We exploit this insight by reinforcing the impact of concept-relevant layers in Concept Guidance (CoG), a precise, target-specific guidance method that works for models out-of-the-box without additional training, external models, gradients, or prompt engineering. CoG first quantifies each layer's concept-specific impact and then guides denoising using a weighted combination of predictions generated with concept-relevant layers skipped. We demonstrate performance increases across various targets and popular models like PixArt-alpha, SD3, SD3.5, and FLUX.1-dev. Code is available at https://github.com/CompVis/concept_guidance
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