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from datasets import load_dataset
from torchvision.transforms import InterpolationMode
from torchvision.transforms import functional as F
def art2prompt(example):
image = example["image"]
image = F.resize(image, 512, InterpolationMode.LANCZOS)
artist = example["artist_str"].replace("-", " ").title()
if example["genre_str"] == "Unknown Genre":
genre = "painting"
else:
genre = example["genre_str"].replace("_", " ")
style = example["style_str"].replace("_", " ").lower()
captions = [
# a landscape in the style of Vincent Van Gogh
f"a {genre} in the style of {artist}",
# a landscape in the style of realism
f"a {genre} in the style of {style}",
# a realism painting by Vincent Van Gogh
f"a {style} painting by {artist}",
# a landscape by Vincent Van Gogh
f"a {genre} by {artist}",
]
return {"text": captions, "image": image}
dataset = load_dataset("huggan/wikiart", split="train")
# map the integer labels to their strings
dataset = dataset.map(
lambda ex: {
"artist_str": dataset.features["artist"].int2str(ex["artist"]),
"genre_str": dataset.features["genre"].int2str(ex["genre"]),
"style_str": dataset.features["style"].int2str(ex["style"]),
},
remove_columns=["artist", "genre", "style"],
)
# generate prompts from attributes
dataset = dataset.map(
art2prompt, remove_columns=["artist_str", "genre_str", "style_str"], num_proc=8, writer_batch_size=100
)
dataset.push_to_hub("fusing/wikiart_captions", split="train")
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