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name: ddpm_adaptation_protocol description: Build shared noised DDPM adaptation batches and reconstruct clean-image predictions for source and adapted denoisers.

DDPM Adaptation Protocol

Use this skill when implementing or auditing DDPM-PA style few-shot adaptation. It prepares the shared noised inputs used by a frozen source denoiser and a trainable adapted denoiser, then reconstructs predicted clean images with the paper's Equation 15.

Do not use this skill to compute pairwise KL or high-frequency losses directly; those are separate module contracts. This skill owns only the noising, denoiser-call alignment, reconstruction formula, and metadata needed by downstream losses.

Inputs

• Clean images as nested Python lists or numeric arrays shaped [batch, channels, height, width].
• alpha_bar_t with 0 < alpha_bar_t <= 1.
• A deterministic noise tensor with the same shape as the image batch, or a caller-controlled seed in higher-level code.
• Source and adapted predicted-noise tensors/callable outputs with the same shape as x_t.

Outputs

• Noised batch x_t.
• Reconstructed source_x0_hat and adapted_x0_hat.
• Metadata including shape and alpha_bar_t.

Workflow

1. Validate that image, noise, and predicted-noise tensors share shape.
2. Compute x_t = sqrt(alpha_bar_t) * x0 + sqrt(1 - alpha_bar_t) * epsilon.
3. Reconstruct x0_hat = (x_t - sqrt(1 - alpha_bar_t) * predicted_epsilon) / sqrt(alpha_bar_t) for each branch.
4. Pass only tensors and metadata downstream; never update source parameters here.

Validation

Run:

python scripts/ddpm_protocol.py --smoke
python -m pytest tests

The tests verify deterministic noising and exact reconstruction when predicted noise equals the injected noise.

Limitations

The script uses standard-library nested-list math for portability. Full diffusion training code may wrap the same formulas in NumPy, PyTorch, or JAX, but must preserve shared x_t and branch-aligned reconstruction semantics.