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Philip Teare

2 accepted papers

2026

Causal-Adapter: Taming Text-to-Image Diffusion for Faithful Counterfactual Generation

ICML 2026poster

We present Causal-Adapter, a modular framework that adapts frozen text-to-image diffusion backbones for counterfactual image generation. Our method enables causal interventions on target attributes while preserving all other aspects of the image, including the core identity. In contrast to prior app…

Cited by 0SourceScholar
2026

Stop the Flip-Flop: Context-Preserving Verification for Fast Revocable Diffusion Decoding

ICML 2026poster

Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelism often harms quality. Revocable decoding mitigates this by rechecking earlier tokens, yet we observe that existing verification schemes frequently trigger fl…

Cited by 0SourceScholar