Release 2.0.8 - The Dawn of Diffusion Models

Announcements
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Release notes :sparkles:

The next BayesFlow release adds substantial new functionality, largely centered on diffusion models (score-based, flow-matching, consistency) for simulation-based inference (see [2512.20685] Diffusion Models in Simulation-Based Inference: A Tutorial Review for more details) and various improvements to the codebase.

Added :new_button:

  • Guidance support for diffusion models.
  • Optional batched sampling for large inference datasets, with progress tracking and wall-clock time reporting.
  • Additional progress bars for sampling workflows.
  • New options for flow matching, including conditional optimal transport.
  • A more robust C2ST test, plus new diagnostics.

Changed :wrench:

  • Improved diffusion-model solvers.
  • Vastly improved default backbones for diffusion-like models (diffusion, flow matching, consistency). Expect significant performance boosts! (BREAKING for loading old models trained in previous versions)
  • Improved internal handling of inference phases, enabling arbitrary subnetworks that can process targets, conditions, and time in flexible ways.
  • Expanded sampling flexibility (e.g., time-varying parameters, images, and other structured targets/conditions) and sample_shape option.

Maintenance :broom:

  • Increased test coverage.
  • Multiple bug fixes.

As always, happy amortizing!

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