CausalCtrl: Causality-Aware Control Framework for Text-Guided Visual Editing

Text-guided visual editing aims to modify visual content according to a target prompt while faithfully preserving the structure and identity of the source image or video. However, existing methods ignore confounding effects brought from the pretrained model, i.e., harmful biases learned from the pretraining datasets, leading to spurious correlations during the editing processing. To address this issue, we introduce CausalCtrl, a novel training-free framework that reformulates text-guided visual editing from a causal inference perspective. The core idea is to leverage frontdoor adjustment to estimate the interventional distribution of the output, effectively blocking the influence of hidden confounders introduced by the pretrained model. Specifically, we first design a dual-branch inversion mechanism that disentangles the source content and target semantics into two separate latent embeddings to simplify the sampling space of interventional operation, and perform unbiased denoising through their controlled interaction. Besides, we propose a Structured Attention Injection Module (SAIM) that adaptively identifies and amplifies dominant attention heads using a lightweight SVD-based top-K selection strategy. Extensive experiments on several challenging image and video editing benchmarks demonstrate that CausalCtrl consistently outperforms existing methods in both target semantic alignment and source content preservation, validating the effectiveness of causal intervention in this task.