Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time

• URL: http://arxiv.org/abs/2607.26647v1
• Date: Wed, 29 Jul 2026 09:08:57 GMT
• Title: Anchoring and Steering Diffusion: Enhancing the Faithfulness of Text-to-Image Generation at Inference Time
• Abstract summary: AnchorSteer is a training-free framework that exerts fine-grained control over.<n>textbfAnchorSteer and its components.<n>Experiments on GenEval and T2I-CompBench++ demonstrate that AnchorSteer consistently outperforms existing baselines in text--image alignment.
• Score: 16.04233421396159
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While text-to-image diffusion models achieve impressive visual quality, they frequently struggle to maintain precise alignment with complex compositional prompts. An effective strategy is to improve the inference process of diffusion models, thereby better leveraging their pretrained priors to address misalignment. Existing training-free methods can be divided into two categories. The first category focuses on improving the randomly sampled initial noise, either performing costly search over noise pools or manipulating sampled noise without ensuring reliable semantic injection. The second category focuses on improving the denoising trajectory, lacking explicit mechanisms to timely diagnose and correct semantic errors. we propose \textbf{AnchorSteer}, a training-free framework that exerts fine-grained control over \textbf{both initialization} and \textbf{the denoising trajectory}. AnchorSteer consists of two synergistic components: \textbf{Semantic Anchoring} replaces uninformative Gaussian noise with text-aligned initializations via CLIP-based prior extraction and a novel Latent-Prior Score Distillation Sampling (LP-SDS) objective. Specifically, LP-SDS distills CLIP visual priors into the knowledge distribution of diffusion models, mitigating the domain gap between CLIP-based priors and diffusion-based priors. \textbf{Reflective Steering} transforms passive denoising with an active Think--Erase--Retouch loop that enables mid-generation self-correction. It leverages VLM-based diagnosis to detect semantic deviations and performs targeted latent refinement to suppress erroneous content and recover missing attributes. Extensive experiments on GenEval and T2I-CompBench++ demonstrate that AnchorSteer consistently outperforms existing baselines in text--image alignment while preserving high visual quality.

Related papers

• UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation [90.0896070825457]
  We propose a unified noise-driven framework that rethinks safety mechanisms through the lens of noise dynamics in diffusion processes.<n>Our key insight is that early-stage predicted noise exhibits inherent separability between normal and sexually explicit content.<n>We develop a lightweight noise-based detector achieving superior accuracy with virtually no computational overhead.
  arXiv  Detail & Related papers  (2026-07-18T14:03:39Z)
• Probing Diffusion Denoising Dynamics for Contrastive Representation Learning [48.679227622825806]
  We study how can the denoising dynamics of a pretrained diffusion model be adapted to support discriminative representation learning.<n>We present D$3$CL as an investigation of this question.<n>We show that noise-level contrastive learning can structure denoising representations for discriminative tasks while maintaining generative performance.
  arXiv  Detail & Related papers  (2026-07-10T03:23:44Z)
• A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning [2.436631469537453]
  We propose textbfSFT+RL, a two-stage robust UDA framework that integratesSupervised Fine Tuning and Reinforcement Learning.<n>textbfSFT+RL achieves average improvements of textbf10.2% in clean accuracy and textbf15.8% in adversarial robustness across three datasets.
  arXiv  Detail & Related papers  (2026-07-03T20:56:12Z)
• PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution [51.96078493242164]
  PRISM is a single-step diffusion-based Text-SR framework.<n> PRISM achieves state-of-the-art performance with millisecond-level inference.
  arXiv  Detail & Related papers  (2026-05-13T05:31:06Z)
• V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising [65.5867130156805]
  We present V-Co, a systematic study of visual co-denoising in a unified JiT-based framework.<n>Our study reveals four key ingredients for effective visual co-denoising.<n>V-Co outperforms the underlying pixel-space diffusion baseline and strong prior pixel-diffusion methods.
  arXiv  Detail & Related papers  (2026-03-17T17:01:54Z)
• Context-Aware Initialization for Reducing Generative Path Length in Diffusion Language Models [0.0]
  Large Language Models (DLLMs) enable fully parallel token decoding but often remain impractical at inference time.<n>Most existing acceleration methods focus on traversing this generative trajectory more efficiently via improved solvers or sampling strategies.<n>We propose a training-free interface that injects prompt-conditioned priors from a lightweight auxiliary model into the diffusion initialization.<n>Because injected priors can be imperfect and unmask-only decoding can over-commit early, we also introduce a simple confidence-based remasking mechanism as a form of prior skepticism.
  arXiv  Detail & Related papers  (2025-12-22T03:45:04Z)
• InfSplign: Inference-Time Spatial Alignment of Text-to-Image Diffusion Models [27.206678799411645]
  InfSplign is a training-free inference-time method for text-to-image models.<n>It improves spatial alignment by adjusting the noise through a compound loss in every denoising step.<n>It achieves substantial performance gains over the strongest existing inference-time baselines.
  arXiv  Detail & Related papers  (2025-12-19T17:52:43Z)
• Robustifying Diffusion-Denoised Smoothing Against Covariate Shift [3.2010481260411834]
  We propose a novel adversarial objective function focused on the added noise of the denoising diffusion model.<n>Our method significantly improves certified accuracy across three standard classification benchmarks.
  arXiv  Detail & Related papers  (2025-09-13T17:27:37Z)
• Active Adversarial Noise Suppression for Image Forgery Localization [56.98050814363447]
  We introduce an Adversarial Noise Suppression Module (ANSM) that generate a defensive perturbation to suppress the attack effect of adversarial noise.<n>To our best knowledge, this is the first report of adversarial defense in image forgery localization tasks.
  arXiv  Detail & Related papers  (2025-06-15T14:53:27Z)
• Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion [52.315729095824906]
  MLLM Semantic-Corrected Ping-Pong-Ahead Diffusion (PPAD) is a novel framework that introduces a Multimodal Large Language Model (MLLM) as a semantic observer during inference.<n>It performs real-time analysis on intermediate generations, identifies latent semantic inconsistencies, and translates feedback into controllable signals that actively guide the remaining denoising steps.<n>Extensive experiments demonstrate PPAD's significant improvements.
  arXiv  Detail & Related papers  (2025-05-26T14:42:35Z)
• Improving Consistency in Diffusion Models for Image Super-Resolution [28.945663118445037]
  We observe two kinds of inconsistencies in diffusion-based methods.<n>We introduce ConsisSR to handle both semantic and training-inference consistencies.<n>Our method demonstrates state-of-the-art performance among existing diffusion models.
  arXiv  Detail & Related papers  (2024-10-17T17:41:52Z)
• Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting [133.55037976429088]
  We investigate the adversarial robustness of vision transformers equipped with BERT pretraining (e.g., BEiT, MAE) A surprising observation is that MAE has significantly worse adversarial robustness than other BERT pretraining methods. We propose a simple yet effective way to boost the adversarial robustness of MAE.
  arXiv  Detail & Related papers  (2023-08-20T16:27:17Z)
• Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment [53.401889855278704]
  Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories with the help of limited available samples. We propose a two-stage background suppression and foreground alignment framework, which is composed of a background activation suppression (BAS) module, a foreground object alignment (FOA) module, and a local to local (L2L) similarity metric. Experiments conducted on multiple popular fine-grained benchmarks demonstrate that our method outperforms the existing state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2022-10-04T07:54:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.