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cast

Build consistent characters, environments and props in Guaardvark's Cast Library and train LoRAs for them locally (reference photos → vision bible → sample plan → approved samples → training). Use when the user wants the same face or object across images, videos, a music video, or a Film Crew production, or asks to "train a LoRA".

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guaardvark/guaardvark
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SKILL.md (original)

Review the instructions the agent will receive before you install.

Cast Library and LoRA training

Read setup first; training needs the lora_trainer plugin (CUDA, bf16) and an installed train-ready base (Z-Image, SDXL or FLUX family). B=${GUAARDVARK_URL:-http://localhost:5000}.

The pipeline

  1. Create the subject 
    curl -s -X POST $B/api/cast-library/subjects -H 'Content-Type: application/json' -d '{
  "kind": "character", "name": "Mara", "description": "late 30s, short grey hair, freckles",
  "trigger_word": "mara_v1", "voice_id": null
}'
    kind is character, environment or prop. GET $B/api/cast-library lists subjects; the numeric id is what generate_image and batch routes take as subject_ids.
  2. Upload reference images (5 to 20 clear shots, varied angles, same subject): curl -s -X POST $B/api/cast-library/subjects/$ID/upload-refs -F files=@1.jpg -F files=@2.jpg Ask first whether the person consented; do not train on someone who has not.
  3. Vision bible from the refs: POST $B/api/cast-library/subjects/$ID/bible/from-refs (the vision model writes the identity description that every prompt inherits).
  4. Plan and generate training samples: POST .../$ID/plan then POST .../$ID/generate (cancel with .../generate/cancel). Review GET .../$ID/samples; view one with GET .../samples/<sample_id>/image; drop bad ones with DELETE .../samples/<sample_id> or POST .../samples/<sample_id>/regenerate.
  5. Approve samples: POST .../$ID/samples/approve.
  6. Train: POST $B/api/cast-library/subjects/$ID/train with optional {"training_settings": {...}}. A 409 already_training means wait; a train_base_not_ready error names the base model to install first. Cancel: .../train/cancel. Progress: GET $B/api/cast-library/subjects/$ID (status, base model, LoRA path when done).
  7. Use it: generate_image with subject_ids=[ID]; batch image/video routes with subject_ids; the Film Crew casts it; the music-video Director locks it per cut.

Rules

  • Training is a GPU job of tens of minutes; say so and check inspect_gpu for conflicts first.
  • The trigger word is applied by the system when subject_ids is passed; the user does not need to type it.
  • Everything stays local: refs, samples and the LoRA file under data/.

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