Papers › Diffusion Counterfactual Generation with Semantic Abduction

Diffusion Counterfactual Generation with Semantic Abduction

9 Jun 2025arXiv:2506.07883archive 2025-07-28

Rajat Rasal, Avinash Kori, Fabio De Sousa Ribeiro, Tian Xia, Ben Glocker

Counterfactual image generation presents significant challenges, including preserving identity, maintaining perceptual quality, and ensuring faithfulness to an underlying causal model. While existing auto-encoding frameworks admit semantic latent spaces which can be manipulated for causal control, they struggle with scalability and fidelity. Advancements in diffusion models present opportunities for improving counterfactual image editing, having demonstrated state-of-the-art visual quality, human-aligned perception and representation learning capabilities. Here, we present a suite of diffusion-based causal mechanisms, introducing the notions of spatial, semantic and dynamic abduction. We propose a general framework that integrates semantic representations into diffusion models through the lens of Pearlian causality to edit images via a counterfactual reasoning process. To our knowledge, this is the first work to consider high-level semantic identity preservation for diffusion counterfactuals and to demonstrate how semantic control enables principled trade-offs between faithful causal control and identity preservation.

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evaluate_classifier rajatrasal/diffusion-counterfactuals/model/classifier.py official repository unverified MIT (permissive) · 8afb07a694e85597 · report
get_betas rajatrasal/diffusion-counterfactuals/model/utils/diffusion.py official repository unverified MIT (permissive) · cb62c7e81fb0b39f · report
get_torchvision_transforms rajatrasal/diffusion-counterfactuals/model/dataset.py official repository unverified MIT (permissive) · 59fde1116ac21980 · report
load_idx rajatrasal/diffusion-counterfactuals/model/mnist.py official repository unverified MIT (permissive) · 5d47dd69d34460d0 · report
load_image_pillow rajatrasal/diffusion-counterfactuals/model/dataset.py official repository unverified MIT (permissive) · d7b2b0a5c2a807f7 · report
load_morphomnist_like rajatrasal/diffusion-counterfactuals/model/mnist.py official repository unverified MIT (permissive) · 0e3efa6fd931c56f · report
metrics rajatrasal/diffusion-counterfactuals/benchmarking/celeba_f1.py official repository unverified MIT (permissive) · 42a6db49eebe4a0a · report
metrics rajatrasal/diffusion-counterfactuals/benchmarking/celeba_nto.py official repository unverified MIT (permissive) · f1703a9e979afe6b · report
normalise rajatrasal/diffusion-counterfactuals/model/mnist.py official repository unverified MIT (permissive) · e8a4d61a5fec68dd · report

Tasks

Counterfactual ReasoningImage GenerationRepresentation Learning

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CounterfactualsDiffusion

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