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# Copyright 2022 Jay Wang, Evan Montoya, David Munechika, Alex Yang, Ben Hoover, Polo Chau
# MIT License
"""Loading script for DiffusionDB."""
import numpy as np
from json import load, dump
from os.path import join, basename
import datasets
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{wangDiffusionDBLargescalePrompt2022,
title = {{{DiffusionDB}}: {{A}} Large-Scale Prompt Gallery Dataset for Text-to-Image Generative Models},
author = {Wang, Zijie J. and Montoya, Evan and Munechika, David and Yang, Haoyang and Hoover, Benjamin and Chau, Duen Horng},
year = {2022},
journal = {arXiv:2210.14896 [cs]},
url = {https://arxiv.org/abs/2210.14896}
}
"""
# You can copy an official description
_DESCRIPTION = """
DiffusionDB is the first large-scale text-to-image prompt dataset. It contains 2
million images generated by Stable Diffusion using prompts and hyperparameters
specified by real users. The unprecedented scale and diversity of this
human-actuated dataset provide exciting research opportunities in understanding
the interplay between prompts and generative models, detecting deepfakes, and
designing human-AI interaction tools to help users more easily use these models.
"""
_HOMEPAGE = "https://poloclub.github.io/diffusiondb"
_LICENSE = "CC0 1.0"
_VERSION = datasets.Version("0.9.0")
# Programmatically generate the URLs for different parts
# https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/images/part-000001.zip
_URLS = {}
_PART_IDS = range(1, 2001)
for i in _PART_IDS:
_URLS[
i
] = f"https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/images/part-{i:06}.zip"
class DiffusionDBConfig(datasets.BuilderConfig):
"""BuilderConfig for DiffusionDB."""
def __init__(self, part_ids, **kwargs):
"""BuilderConfig for DiffusionDB.
Args:
part_ids([int]): A list of part_ids.
**kwargs: keyword arguments forwarded to super.
"""
super(DiffusionDBConfig, self).__init__(version=_VERSION, **kwargs)
self.part_ids = part_ids
class DiffusionDB(datasets.GeneratorBasedBuilder):
"""A large-scale text-to-image prompt gallery dataset based on Stable Diffusion."""
BUILDER_CONFIGS = []
# Programmatically generate configuration options (HF requires to use a string
# as the config key)
for num_k in [1, 5, 10, 50, 100, 500, 1000]:
for sampling in ["first", "random"]:
num_k_str = f"{num_k}k" if num_k < 1000 else f"{num_k // 1000}m"
if sampling == "random":
# Name the config
cur_name = "random_" + num_k_str
# Add a short description for each config
cur_description = (
f"Random {num_k_str} images with their prompts and parameters"
)
# Sample part_ids
part_ids = np.random.choice(_PART_IDS, num_k, replace=False).tolist()
else:
# Name the config
cur_name = "first_" + num_k_str
# Add a short description for each config
cur_description = f"The first {num_k_str} images in this dataset with their prompts and parameters"
# Sample part_ids
part_ids = _PART_IDS[1 : num_k + 1]
# Create configs
BUILDER_CONFIGS.append(
DiffusionDBConfig(
name=cur_name,
part_ids=part_ids,
description=cur_description,
),
)
# For the 2k option, random sample and first parts are the same
BUILDER_CONFIGS.append(
DiffusionDBConfig(
name="all",
part_ids=_PART_IDS,
description="All images with their prompts and parameters",
),
)
# Default to only load 1k random images
DEFAULT_CONFIG_NAME = "random_1k"
def _info(self):
"""Specify the information of DiffusionDB."""
features = datasets.Features(
{
"image": datasets.Image(),
"prompt": datasets.Value("string"),
"seed": datasets.Value("int64"),
"step": datasets.Value("int64"),
"cfg": datasets.Value("float32"),
"sampler": datasets.Value("string"),
},
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
# If several configurations are possible (listed in BUILDER_CONFIGS),
# the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to
# download and extract URLS It can accept any type or nested list/dict
# and will give back the same structure with the url replaced with path
# to local files. By default the archives will be extracted and a path
# to a cached folder where they are extracted is returned instead of the
# archive
# Download and extract zip files of all sampled part_ids
data_dirs = []
json_paths = []
for cur_part_id in self.config.part_ids:
cur_url = _URLS[cur_part_id]
data_dir = dl_manager.download_and_extract(cur_url)
data_dirs.append(data_dir)
json_paths.append(join(data_dir, f"part-{cur_part_id:06}.json"))
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"data_dirs": data_dirs,
"json_paths": json_paths,
},
),
]
def _generate_examples(self, data_dirs, json_paths):
# This method handles input defined in _split_generators to yield
# (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself,
# but must be unique for each example.
# Iterate through all extracted zip folders
num_data_dirs = len(data_dirs)
assert num_data_dirs == len(json_paths)
for k in range(num_data_dirs):
cur_data_dir = data_dirs[k]
cur_json_path = json_paths[k]
json_data = load(open(cur_json_path, "r", encoding="utf8"))
for img_name in json_data:
img_params = json_data[img_name]
img_path = join(cur_data_dir, img_name)
# Yields examples as (key, example) tuples
yield img_name, {
"image": {"path": img_path, "bytes": open(img_path, "rb").read()},
"prompt": img_params["p"],
"seed": int(img_params["se"]),
"step": int(img_params["st"]),
"cfg": float(img_params["c"]),
"sampler": img_params["sa"],
}
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