Adversarial Diffusion Distillation: High Fidelity Real Time Image Synthesis

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Издатель Sep 12, 2026

Adversarial Diffusion Distillation (ADD) is a training method that enables high-fidelity image generation in as few as one to four steps. Traditional diffusion models are often slow due to their iterative nature. By combining an adversarial loss for crisp realism and a distillation loss that extracts knowledge from a larger "teacher" model, ADD achieves real-time synthesis without sacrificing detail. This hybrid approach allows the student model to produce superior results compared to existing fast-sampling techniques like Latent Consistency Models.