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https://github.com/brycedrennan/imaginAIry
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You can use `{}` to randomly pull values from lists. A list of values separated by `|` and enclosed in `{ }` will be randomly drawn from in a non-repeating fashion. Values that are surrounded by `_ _` will pull from a phrase list of the same name. Folders containing .txt phraselist files may be specified via
`--prompt_library_path`. The option may be specified multiple times. Built-in categories:
3d-term, adj-architecture, adj-beauty, adj-detailed, adj-emotion, adj-general, adj-horror, animal, art-movement,
art-site, artist, artist-botanical, artist-surreal, aspect-ratio, bird, body-of-water, body-pose, camera-brand,
camera-model, color, cosmic-galaxy, cosmic-nebula, cosmic-star, cosmic-term, dinosaur, eyecolor, f-stop,
fantasy-creature, fantasy-setting, fish, flower, focal-length, food, fruit, games, gen-modifier, hair, hd,
iso-stop, landscape-type, national-park, nationality, neg-weight, noun-beauty, noun-fantasy, noun-general,
noun-horror, occupation, photo-term, pop-culture, pop-location, punk-style, quantity, rpg-item, scenario-desc,
skin-color, spaceship, style, tree-species, trippy, world-heritage-site
Examples:
`imagine "a {red|black} dog" -r 2 --seed 0` will generate both "a red dog" and "a black dog"
`imagine "a {_color_} dog" -r 4 --seed 0` will generate four, different colored dogs. The colors will eb pulled from an included
phraselist of colors.
`imagine "a {_spaceship_|_fruit_|hot air balloon}. low-poly" -r 4 --seed 0` will generate images of spaceships or fruits or a hot air balloon
Credit to [noodle-soup-prompts](https://github.com/WASasquatch/noodle-soup-prompts/) where most, but not all, of the wordlists originate.
73 lines
1.8 KiB
Python
73 lines
1.8 KiB
Python
import gzip
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import json
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import os.path
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import time
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from contextlib import contextmanager
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CURDIR = os.path.dirname(__file__)
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excluded_prefixes = ["identity", "gender", "body", "celeb", "color"]
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excluded_words = {
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"sex",
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"sexy",
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"sex appeal",
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"sex symbol",
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"young",
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"youth",
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"youthful",
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"child",
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"baby",
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}
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category_renames = {
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"3d-terms": "3d-term",
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"animals": "animal",
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"camera": "camera-model",
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"camera-manu": "camera-brand",
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"cosmic-terms": "cosmic-term",
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"details": "adj-detailed",
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"foods": "food",
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"games": "video-game",
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"movement": "art-movement",
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"noun-emote": "adj-emotion",
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"natl-park": "national-park",
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"portrait-type": "body-pose",
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"punk": "punk-style",
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"site": "art-site",
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"tree": "tree-species",
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"water": "body-of-water",
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"wh-site": "world-heritage-site",
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}
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@contextmanager
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def timed(description):
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start = time.perf_counter()
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yield
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end = time.perf_counter()
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duration = end - start
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print(f"{description} {duration:2f}")
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def make_txts():
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src_json = f"{CURDIR}/../downloads/noodle-soup-prompts/nsp_pantry.json"
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dst_folder = f"{CURDIR}/../imaginairy/vendored/noodle_soup_prompts"
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with open(src_json, "r", encoding="utf-8") as f:
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prompts = json.load(f)
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categories = []
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for c in prompts.keys():
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if any(c.startswith(p) for p in excluded_prefixes):
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continue
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categories.append(c)
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categories.sort()
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for c in categories:
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print((c, len(prompts[c])))
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filtered_phrases = [p.lower() for p in prompts[c] if p not in excluded_words]
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renamed_c = category_renames.get(c, c)
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with gzip.open(f"{dst_folder}/{renamed_c}.txt.gz", "wb") as f:
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for p in filtered_phrases:
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f.write(f"{p}\n".encode("utf-8"))
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if __name__ == "__main__":
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make_txts()
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