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name: cli-workflows description: "Use the deep-daze imagine CLI for text, image, story, priming, progress, and output workflow command construction." disable-model-invocation: true metadata: disco-role: operating license: MIT

CLI workflows

Use this sub-skill when the task is to construct, adapt, or recover a deep-daze imagine command-line workflow. It covers text prompts, image-target conditioning, text+image conditioning, start-image priming, story mode, progress/GIF/video outputs, output naming, overwrite prompts, and headless-safe command construction.

For long flag tables and defaults, read references/cli-reference.md. For practical commands, read references/workflow-recipes.md. For failures, read references/troubleshooting.md. To build a command without running generation, use scripts/build_imagine_command.py.

Routing boundaries

• Use this sub-skill for the console entry point imagine and the CLI function arguments exposed by that command.
• Route Python-only DeepDaze / Imagine class usage, custom encodings, and method-level adaptation to ../python-api/SKILL.md.
• Route CLIP model cache, model download, device, backend, dependency, and hardware setup issues to ../runtime-and-models/SKILL.md.

Operating defaults

• Prefer short, explicit, reproducible commands before expensive generation: set small epochs, iterations, image_width, batch_size, and save_every until the prompt and output behavior are confirmed.
• On headless or automated systems, set --open_folder=False so the CLI does not try to launch a desktop file browser.
• To avoid blocking on an overwrite prompt, either use --save_date_time=True for unique names or intentionally set --overwrite=True only when replacing the existing output is acceptable.
• Keep prompts under CLIP's 77-token context in normal mode. For longer prose, use --create_story=True with --save_progress=True and optionally a --story_separator.

Command-builder quick start

From this sub-skill directory, print a safe smoke-test command without starting generation:

python scripts/build_imagine_command.py --prompt "a small house in a misty forest"

For a low-memory command using an image target and start-image priming:

python scripts/build_imagine_command.py \
  --prompt "a watercolor night sky" \
  --img inputs/target.jpg \
  --start-image inputs/prime.jpg \
  --preset low-vram

Copy the printed imagine ... command, run it from the directory where output artifacts should be written, and keep expensive runs bounded until the runtime/model setup is known to be ready.