Semantic Guidance Tuning for Text-To-Image Diffusion Models
This addresses the issue of poor semantic adherence in text-to-image generation for users relying on accurate prompt-to-image translation, though it is an incremental improvement over existing methods.
The paper tackles the problem of text-to-image diffusion models often misrepresenting or overlooking specific attributes in prompts, proposing a training-free approach that modulates guidance direction during inference to improve semantic alignment, with extensive experimentation validating the improvement.
Recent advancements in Text-to-Image (T2I) diffusion models have demonstrated impressive success in generating high-quality images with zero-shot generalization capabilities. Yet, current models struggle to closely adhere to prompt semantics, often misrepresenting or overlooking specific attributes. To address this, we propose a simple, training-free approach that modulates the guidance direction of diffusion models during inference. We first decompose the prompt semantics into a set of concepts, and monitor the guidance trajectory in relation to each concept. Our key observation is that deviations in model's adherence to prompt semantics are highly correlated with divergence of the guidance from one or more of these concepts. Based on this observation, we devise a technique to steer the guidance direction towards any concept from which the model diverges. Extensive experimentation validates that our method improves the semantic alignment of images generated by diffusion models in response to prompts. Project page is available at: https://korguy.github.io/