Bridging Restoration and Generation Manifolds in One-Step Diffusion for Real-World Super-Resolution

• URL: http://arxiv.org/abs/2604.24136v1
• Date: Mon, 27 Apr 2026 07:43:00 GMT
• Title: Bridging Restoration and Generation Manifolds in One-Step Diffusion for Real-World Super-Resolution
• Abstract summary: Recent single-step distillation accelerates inference but faces a stark perception-distortion trade-off.<n>We present Adaptive Inversion and Degradation-aware Sampling for Real-ISR (IDaS-SR)<n>IDaS-SR is a one-step framework bridging the deterministic restoration and generation bottlenecks.
• Score: 22.963799508399365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR) but suffer from computational bottlenecks due to iterative sampling. Recent single-step distillation accelerates inference but faces a stark perception-distortion trade-off due to rigid timestep initialization, distributional trajectory mismatches, and fragile stochastic modulation. To address this, we present Adaptive Inversion and Degradation-aware Sampling for Real-ISR (IDaS-SR), a one-step framework bridging the deterministic restoration and stochastic generation manifolds. At its core, the Manifold Inversion Noise Estimator (MINE) resolves these initialization and trajectory mismatches by predicting a severity-aware timestep and inversion noise, precisely anchoring low-quality latents onto the diffusion trajectory. Furthermore, to mitigate fragile stochastic modulation, we propose CHARIOT, a continuous generative steering mechanism. By rescheduling trajectories and interpolating noise, it enables explicit navigation of the perception-distortion boundary without compromising structural priors. Extensive experiments demonstrate that IDaS-SR outperforms state-of-the-art methods, seamlessly transitioning from a rigorous structural restorer to a sophisticated texture hallucinator in a single inference step.

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