executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
model-assets-and-runtimeSet up and inspect min(DALL·E) model assets, tokenizer, model variants, runtime device/dtype decisions, and internal shapes without unnecessary downloads.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when the task is about min(DALL·E) model weight caches, tokenizer assets, model variant sizing, device/dtype choices, memory/runtime setup, or internal tensor shapes. The goal is to inspect and prepare safely before running expensive generation.
is_mega constants, dtype/device choices, and no-download preflight checks, read references/runtime-and-assets.md.MinDalle or load model weights.MinDalle.generate_image* usage, stream consumption, seed/top-k/temperature recipe choices, and saving generated PIL/tensor outputs belong in ../text-to-image-generation/SKILL.md.../deployment-and-interfaces/SKILL.md.models_root layout and run the tokenizer smoke script before constructing a full MinDalle object.MinDalle(...) construction as potentially network- and memory-active. Even tokenizer initialization can contact the Hugging Face asset endpoint; is_reusable=True can download and load all major .pt weights..pt files just to prove that a cache path exists. Check existence, non-zero size, and obvious partial-download symptoms first; only load weights when the user approved full runtime setup.device, dtype, is_mega, and is_reusable from the user's hardware and budget rather than from examples copied blindly.