{"slug": "swift-self-guided-weak-to-strong-diffusion-models-via-target-extrapolation", "title": "SWIFT: Self-guided Weak-to-strong dIFfusion models via Target extrapolation", "summary": "Researchers introduced Swift, a self-guided training method that uses a frozen weak denoiser to improve a stronger diffusion model without adding inference-time sampling cost. Swift targets the high computational expense of training high-quality diffusion models and the extra sampling cost that inference-time guidance typically adds. The method is described as simple yet effective, though the source excerpt provides no benchmark figures or quantitative results.", "body_md": "Training high-quality diffusion models is computationally expensive, and inference-time guidance often adds further sampling cost. In this work, we introduce Swift, a simple yet effective self-guided training method that uses a frozen weak denoiser to improve a stronger model without adding inferenc", "url": "https://wpnews.pro/news/swift-self-guided-weak-to-strong-diffusion-models-via-target-extrapolation", "canonical_source": "https://aiflash.com/news/136478/", "published_at": "2026-10-21 03:00:39+00:00", "updated_at": "2026-10-21 03:19:35.407963+00:00", "lang": "en", "topics": ["machine-learning", "generative-ai", "ai-research", "artificial-intelligence"], "entities": ["Swift"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/swift-self-guided-weak-to-strong-diffusion-models-via-target-extrapolation", "markdown": "https://wpnews.pro/news/swift-self-guided-weak-to-strong-diffusion-models-via-target-extrapolation.md", "text": "https://wpnews.pro/news/swift-self-guided-weak-to-strong-diffusion-models-via-target-extrapolation.txt", "jsonld": "https://wpnews.pro/news/swift-self-guided-weak-to-strong-diffusion-models-via-target-extrapolation.jsonld"}}