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name: adaptive_unet_spec description: Validate paper-critical guided diffusion UNet architecture flags before claiming architecture-faithful recovery or sampling.

Adaptive UNet Architecture Specification

Use this skill when checking whether a guided-diffusion configuration preserves the architecture choices emphasized in Diffusion Models Beat GANs on Image Synthesis. It is useful for module planning, recovery manifests, and ablation/stress checks.

Inputs

• Resolution, base channels, residual block count, and attention resolutions.
• Flags for use_scale_shift_norm, resblock_updown, learn_sigma, and class conditioning.
• A declaration of whether the run is full, reduced, or proxy.

Outputs

• Normalized spec dictionary.
• ok boolean for paper-like architecture claims.
• Warnings describing omissions that are acceptable only for declared proxies.

Workflow

1. Normalize CLI or JSON fields into a consistent spec.
2. Require scale-shift adaptive normalization for paper-like guided diffusion.
3. Require attention metadata for ImageNet-style conditional synthesis.
4. Require residual up/downsampling and learned sigma for full high-resolution claims.
5. If a reduced proxy does not instantiate a UNet, mark the module as cross-checked instead of fully executed.

Validation

Run python scripts/architecture_spec.py --example paper128. The tests check a passing paper-like spec and failing degraded specs.

Limitations

This skill does not build neural layers or load checkpoints. It prevents architecture metadata drift and records which architecture components were omitted in bounded recovery.