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name: bootstrapped-texture description: "Plan DreamCraft3D texture boosting with Zero123++ multiviews, DreamBooth LoRA, and model artifacts." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Bootstrapped Texture

Use this sub-skill when a DreamCraft3D task involves optional texture boosting, Janus/multiview consistency mitigation, Zero123++ image-to-multiview generation, DreamBooth/LoRA personalization, Stable Diffusion BSD guidance, or model artifact planning.

Read first

• Read references/dreambooth-and-multiview.md for the optional multiview-to-DreamBooth workflow and how LoRA weights plug back into staged generation.
• Read references/model-artifacts.md for Stable Zero123, DeepFloyd IF, Stable Diffusion, Omnidata, and local cache/checkpoint expectations.
• Read references/troubleshooting.md when the optional branch fails because of Janus artifacts, local_files_only, hard-coded GPU ids, xformers/bitsandbytes, OOM, or invalid multiview images.
• Use scripts/plan_texture_boosting.py to produce a non-mutating prerequisite report and suggested commands. It does not download, generate images, or train LoRA.

When to use this sub-skill

Use it for requests like:

• "The stage-1 result has the Janus problem; should I train a custom prior?"
• "Plan the Zero123++ multiview and DreamBooth LoRA branch."
• "Where should Stable Zero123 or Omnidata checkpoints live?"
• "How do I pass LoRA weights into a DreamCraft3D launch command?"
• "Why does img_to_mv.py fail on cuda:1 or local model cache?"

Safe protocol

1. First make the core pipeline route explicit with generation-pipeline. Texture boosting is optional and expensive.
2. Check whether the failure is actually an image/sidecar issue before starting multiview generation.
3. Plan model artifacts and device ids. The source multiview helper uses local_files_only=True and hard-codes cuda:1, so future agents should adapt the command/device carefully.
4. Only run Zero123++ generation or DreamBooth/LoRA training after the user approves GPU time, model downloads/cache use, and output locations.

Quick planner

python <skill-dir>/sub-skills/bootstrapped-texture/scripts/plan_texture_boosting.py \
  --image-path load/images/mushroom_log_rgba.png \
  --prompt "a photo of mushroom" \
  --instance-dir .cache/temp \
  --lora-output-dir .cache/if_dreambooth_mushroom

Use --json for structured output.

Route elsewhere

• Core staged command chain and checkpoint handoff: generation-pipeline.
• Preprocessed image sidecars: image-preparation.
• Exporting meshes or summarizing outputs: export-and-evaluation.
• Docker, Gradio, and broad dependency triage: interfaces-and-monitoring.