Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models

• URL: http://arxiv.org/abs/2506.14399v4
• Date: Tue, 30 Sep 2025 13:50:06 GMT
• Title: Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models
• Authors: Tian Xia, Fabio De Sousa Ribeiro, Rajat R Rasal, Avinash Kori, Raghav Mehta, Ben Glocker,
• Abstract summary: CFG prescribes a global guidance scale for all attributes, leading to significant spurious changes in inferred counterfactuals.<n>We propose Decoupled-Free Guidance (DCFG), a flexible and model-agnostic guidance technique that enables attribute-wise control following a causal graph.
• Score: 14.792134583650787
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Counterfactual generation aims to simulate realistic hypothetical outcomes under causal interventions. Diffusion models have emerged as a powerful tool for this task, combining DDIM inversion with conditional generation and classifier-free guidance (CFG). In this work, we identify a key limitation of CFG for counterfactual generation: it prescribes a global guidance scale for all attributes, leading to significant spurious changes in inferred counterfactuals. To mitigate this, we propose Decoupled Classifier-Free Guidance (DCFG), a flexible and model-agnostic guidance technique that enables attribute-wise control following a causal graph. DCFG is implemented via a simple attribute-split embedding strategy that disentangles semantic inputs, enabling selective guidance on user-defined attribute groups.

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