Deep Daze

Use this repo skill when a task involves the deep-daze / deep_daze package: CLIP-guided text-to-image generation, image-conditioned generation, start-image priming, story mode, the imagine command, or programmatic Imagine and DeepDaze usage.

Deep Daze combines OpenAI CLIP guidance with a Siren generator. Generation is an optimization run, not a one-shot inference call: it can download CLIP weights, consume substantial VRAM or CPU time, and write image/progress artifacts in the current working directory.

Quick install and identity checks

Install the public package in the user's environment:

python -m pip install deep-daze

Minimal import and package-data check:

python - <<'PY'
from deep_daze import DeepDaze, Imagine
from deep_daze.clip import available_models, tokenize
print(DeepDaze.__name__, Imagine.__name__)
print(available_models())
print(tuple(tokenize('a house').shape))
PY

Expected CLIP model names are RN50, RN101, RN50x4, ViT-B/32, and ViT-L/14; ViT-B/32 is the default model. tokenize('a house') should return shape (1, 77).

For a safe inspection that does not download model checkpoints or generate images, run the bundled helper:

python scripts/deep_daze_inspect.py

Read references/usage-overview.md for the package concept map and references/troubleshooting.md for cross-cutting failures.

Route by task

User task	Read next
Verify install/import, dependency presence, CLIP model names, tokenizer package data, cache/download behavior, CPU/GPU backend status, or VRAM expectations.	sub-skills/runtime-and-models/SKILL.md
Build or debug an imagine command for text prompts, image targets, text+image conditioning, start-image priming, story mode, progress frames, GIF/video output, or headless execution.	sub-skills/cli-workflows/SKILL.md
Use deep_daze.Imagine or lower-level DeepDaze from Python, translate CLI settings into code, inspect signatures/defaults, use custom CLIP encodings, or control output paths programmatically.	sub-skills/python-api/SKILL.md

Safe operating defaults

• Start with small epochs, iterations, image_width, and batch_size until the prompt, output path, and runtime are known to work.
• Use open_folder=False in scripts, notebooks, remote shells, and headless agents.
• Use save_date_time=True or a clean output directory to avoid CLI overwrite prompts.
• Keep ordinary prompts within CLIP's 77-token context. Use story mode for long prose, with save_progress=True when the transition frames matter.
• Treat full generation as a potentially long/networked run. It may download a CLIP checkpoint into the user's cache before training starts.

Useful entry points

• CLI: imagine "a house in the forest" --open_folder=False
• Python: from deep_daze import Imagine
• Runtime CLIP utilities: from deep_daze.clip import available_models, tokenize
• Lower-level generator module: from deep_daze import DeepDaze

Validation guidance

Use the bundled inspection helpers and CLI/API probes before expensive runs:

• scripts/deep_daze_inspect.py checks the shared package/runtime surface.
• sub-skills/runtime-and-models/scripts/check_deep_daze_runtime.py performs a deeper runtime preflight.
• sub-skills/cli-workflows/scripts/build_imagine_command.py builds a shell-quoted command without running generation.
• sub-skills/python-api/scripts/probe_api_surface.py checks API signatures/defaults without constructing Imagine.

Provenance and refresh

Read references/repo-provenance.md before using this skill against a different checkout or package version. If the source commit, dirty state, package version, public entry points, CLI signature, or core source layout changed, refresh the repo skill before relying on details.