---

name: python-api description: "Programmatic Deep Daze API use for Imagine and DeepDaze construction, prompt/image/encoding workflows, progress saving, optimizers, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

python-api

Use this sub-skill when a task needs to call Deep Daze from Python rather than assemble shell commands. It covers safe Imagine and DeepDaze construction, prompt/image/custom-encoding choices, story mode, start-image priming, output path helpers, progress saving, optimizers, reproducibility knobs, and API-level failure modes.

Route first

• For command-line invocation, flags, quoting, overwrite prompts, and shell workflow construction, route to ../cli-workflows/SKILL.md.
• For installation, CLIP model cache/download/network diagnosis, CUDA/CPU availability, package compatibility, and backend memory diagnosis, route to ../runtime-and-models/SKILL.md.
• Stay here for Python call structure, constructor arguments, API defaults, helper methods, and code-level troubleshooting after the environment is usable.

Operating sequence

1. Decide the objective input: exactly one of text-only, image-only, combined text+image, or a precomputed CLIP encoding unless deliberately replacing the target later with set_clip_encoding.
2. Pick conservative constructor settings before instantiating Imagine; construction loads CLIP and may perform model retrieval through the runtime cache.
3. Set open_folder=False for noninteractive agents and control the current working directory because images, progress frames, story transitions, GIFs, and videos are written relative to it.
4. Use exact optimizer names: AdamP, Adam, or DiffGrad.
5. Run scripts/probe_api_surface.py --verify when a lightweight API check is needed without constructing Imagine or downloading CLIP weights.
6. Use the references for details:
   • references/api-reference.md for signatures, defaults, helper behavior, and method semantics.
   • references/workflow-recipes.md for Python recipes including low/high VRAM presets.
   • references/troubleshooting.md for constructor, prompt, image, story, optimizer, saving, determinism, and memory pitfalls.

Minimal Python pattern

from deep_daze import Imagine

imagine = Imagine(
    text="a house in the forest",
    epochs=1,
    iterations=100,
    save_every=25,
    save_progress=True,
    open_folder=False,
)
imagine()

Safety notes

• Imagine(...) is not a cheap metadata operation: instantiate only when ready for CLIP loading and potential cache use.
• Regular text prompts must fit CLIP tokenization; use story mode for longer prose but still validate each story segment.
• DeepDaze is the lower-level SIREN module and requires an already loaded CLIP perceptor, normalization transform, input resolution, total-batch count, and batch settings.