Causal-Adapter adapts frozen diffusion backbones via structural causal modeling, prompt-aligned injection, and conditioned token contrastive loss to achieve faithful counterfactual generation with strong attribute control and identity preservation.
Causally steered diffusion for automated video counterfactual generation
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FM-CGM is a framework that uses a large reasoning model and text-to-image diffusion model for zero-shot visual causal reasoning via concept extractor, manipulator, counterfactual generator, and Causal Semantic Guidance mechanism.
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Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation
Causal-Adapter adapts frozen diffusion backbones via structural causal modeling, prompt-aligned injection, and conditioned token contrastive loss to achieve faithful counterfactual generation with strong attribute control and identity preservation.
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Leveraging Foundation Models for Causal Generative Modeling
FM-CGM is a framework that uses a large reasoning model and text-to-image diffusion model for zero-shot visual causal reasoning via concept extractor, manipulator, counterfactual generator, and Causal Semantic Guidance mechanism.