ALADDIN: Adversarial Latent Diffusion Distillation for One-Step Identity Swapping
Read the original on arXiv Computer Vision →ALADDIN is a one‑step face‑swapping method that distills a pre‑trained multi‑step diffusion model into a fast generator trained adversarially with identity and target‑preservation constraints. It offers a controllable trade‑off between identity transfer and attribute preservation via triplet pseudo‑label supervision, and it outperforms other one‑step methods in realism and target preservation while matching the best multi‑step model with far fewer evaluations and training pairs. The authors also uncover a bias in standard evaluation protocols and re‑evaluate baselines with an independent identity encoder.
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