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name: conditioning_dropout_training description: Prepare classifier-free diffusion conditioning-dropout batches and tiny denoising training traces for reduced recovery.

Conditioning Dropout Training

Use this skill when implementing classifier-free guidance training logic from Ho and Salimans: a conditional denoiser is also trained unconditionally by replacing labels with a null condition with probability p_uncond. Do not use it for classifier-guided methods that require external classifier gradients.

Inputs

• Labeled examples or synthetic class means.
• p_uncond in [0, 1].
• Random seed for deterministic recovery.

Outputs

• Records containing original label, effective condition, and null/drop counts.
• Optional tiny trainable denoiser parameters and loss trace.

Workflow

1. Validate p_uncond and seed.
2. Replace each condition with null independently with probability p_uncond.
3. Train or update conditional and unconditional statistics using the effective condition.
4. Record enough counts to prove the joint conditional/unconditional objective ran.

Validation

Run python tests/test_conditioning_dropout_training.py or validate the skill tree with --run-tests.

Limitations

This skill provides mechanism-faithful small-scale utilities. It does not claim full ImageNet diffusion training.