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name: generation description: "Route waveform diffusion generator, text-conditioning, sampling, inpainting, and DiffusionAR tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Generation

Use this sub-skill when a request is about:

• building or smoke-checking an unconditional audio diffusion generator,
• sampling with DiffusionModel.sample,
• enabling text conditioning with classifier-free guidance,
• filling masked waveform gaps with VInpainter,
• or handling the exported but expert-only DiffusionAR path.

Route elsewhere:

• upsampling, vocoding, and autoencoding belong in ../conditioning/.
• pretrained checkpoints, full training runs, benchmark sweeps, and paper-specific recipes are not part of this sub-skill.
• release and import workflows belong to the repo-level skill, not this route.

Quick workflow

1. Start with scripts/tiny_generation_smoke.py for a CPU-only, no-pretrained-weights check.
2. Use references/api-reference.md to confirm constructor names, argument rules, and route behavior.
3. Use references/workflows.md for the smallest working shapes and expected signals.
4. Use references/troubleshooting.md when you hit channel, mask, text-conditioning, or step/schedule errors.

Owned capabilities

• DiffusionModel.forward loss route and DiffusionModel.sample sampler route.
• UNetV0 construction, length checks, attention and cross-attention flags, text-conditioning flags, and resnet_groups constraints.
• VDiffusion, UniformDistribution, LinearSchedule, and VSampler.
• VInpainter source/mask/noise resampling flow.
• Expert notes for DiffusionAR.

Bundled files

• references/api-reference.md
• references/workflows.md
• references/troubleshooting.md
• scripts/tiny_generation_smoke.py