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FLAIR (Flow-Based Latent Alignment for Image Restoration)

Training-free variational posterior sampling framework for image restoration. Uses SD 3.5 Medium as flow-matching prior. No training/fine-tuning needed — works out of the box for SR, inpainting, deblurring.

Paper: arXiv:2506.02680 (2025). Authors: ETH Zurich + Max Planck Institute.

Architecture

Not a new model — a framework that wraps an existing flow-matching generative model:

Degraded image (y) → Forward model A → Variational posterior sampling:
    Prior: SD 3.5 Medium velocity field v(x_t, t)
    Likelihood: consistency with observed pixels
    → DTA + HDC + CRW mechanisms
    → Restored image (x)

Three Mechanisms

  1. DTA (Deterministic Trajectory Adjustment): Reparameterizes variational distribution to recover atypical modes that pure sampling would miss
  2. HDC (Hard Data Consistency): Exact pixel-level consistency with observed (non-degraded) regions
  3. CRW (Calibrated Regularizer Weights): Time-dependent weighting calibrated by offline accuracy estimates

vs Diffusion-Based Restoration ([[RealRestorer]])

Aspect RealRestorer FLAIR
Approach Fine-tuned editing model Training-free posterior sampling
Base model Step1X-Edit (40 GB) SD 3.5 Medium (2B)
Training Requires fine-tuning Zero training
Tasks 9 degradation types (prompted) SR, inpainting, deblur (mathematical)
Speed 28 steps, fast Full SD3.5 loop + optimization, slow

Tasks

  • Super-resolution (tested up to 12× upscaling)
  • Inpainting (with mask)
  • Motion blur removal
  • Text-guided editing (via prompt during inpainting)

VRAM

~24 GB (RTX 4090). Inherits SD 3.5 Medium requirements.

License

Unclear — no LICENSE file. SD 3.5 Medium uses Stability AI Community License (non-commercial or revenue < $1M).

  • GitHub: github.com/prs-eth/FLAIR
  • Demo: huggingface.co/spaces/prs-eth/FLAIR