Datasets:

poloclub
/

diffusiondb

	# Copyright 2022 Jay Wang, Evan Montoya, David Munechika, Alex Yang, Ben Hoover, Polo Chau
	# MIT License
	"""Loading script for DiffusionDB."""

	import re
	import numpy as np
	import pandas as pd

	from json import load, dump
	from os.path import join, basename
	from huggingface_hub import hf_hub_url

	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("0.9.1")

	# Programmatically generate the URLs for different parts
	# hf_hub_url() provides a more flexible way to resolve the file URLs
	# https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/images/part-000001.zip
	_URLS = {}
	_URLS_LARGE = {}
	_PART_IDS = range(1, 2001)
	_PART_IDS_LARGE = range(1, 14001)

	for i in _PART_IDS:
	_URLS[i] = hf_hub_url(
	"poloclub/diffusiondb",
	filename=f"images/part-{i:06}.zip",
	repo_type="dataset",
	)

	for i in _PART_IDS_LARGE:
	if i < 10001:
	_URLS_LARGE[i] = hf_hub_url(
	"poloclub/diffusiondb",
	filename=f"diffusiondb-large-part-1/part-{i:06}.zip",
	repo_type="dataset",
	)
	else:
	_URLS_LARGE[i] = hf_hub_url(
	"poloclub/diffusiondb",
	filename=f"diffusiondb-large-part-2/part-{i:06}.zip",
	repo_type="dataset",
	)

	# Add the metadata parquet URL as well
	_URLS["metadata"] = hf_hub_url(
	"poloclub/diffusiondb", filename="metadata.parquet", repo_type="dataset"
	)

	_URLS_LARGE["metadata"] = hf_hub_url(
	"poloclub/diffusiondb",
	filename="metadata-large.parquet",
	repo_type="dataset",
	)

	_SAMPLER_DICT = {
	1: "ddim",
	2: "plms",
	3: "k_euler",
	4: "k_euler_ancestral",
	5: "ddik_heunm",
	6: "k_dpm_2",
	7: "k_dpm_2_ancestral",
	8: "k_lms",
	9: "others",
	}


	class DiffusionDBConfig(datasets.BuilderConfig):
	"""BuilderConfig for DiffusionDB."""

	def __init__(self, part_ids, is_large, **kwargs):
	"""BuilderConfig for DiffusionDB.
	Args:
	part_ids([int]): A list of part_ids.
	is_large(bool): If downloading data from DiffusionDB Large (14 million)
	**kwargs: keyword arguments forwarded to super.
	"""
	super(DiffusionDBConfig, self).__init__(version=_VERSION, **kwargs)
	self.part_ids = part_ids
	self.is_large = is_large


	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"]:
	for is_large in [False, True]:
	num_k_str = f"{num_k}k" if num_k < 1000 else f"{num_k // 1000}m"
	subset_str = "large_" if is_large else "2m_"

	if sampling == "random":
	# Name the config
	cur_name = subset_str + "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
	total_part_ids = _PART_IDS_LARGE if is_large else _PART_IDS
	part_ids = np.random.choice(
	total_part_ids, num_k, replace=False
	).tolist()
	else:
	# Name the config
	cur_name = subset_str + "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
	total_part_ids = _PART_IDS_LARGE if is_large else _PART_IDS
	part_ids = total_part_ids[1 : num_k + 1]

	# Create configs
	BUILDER_CONFIGS.append(
	DiffusionDBConfig(
	name=cur_name,
	part_ids=part_ids,
	is_large=is_large,
	description=cur_description,
	),
	)

	# Add few more options for Large only
	for num_k in [5000, 10000]:
	for sampling in ["first", "random"]:
	num_k_str = f"{num_k // 1000}m"
	subset_str = "large_"

	if sampling == "random":
	# Name the config
	cur_name = subset_str + "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
	total_part_ids = _PART_IDS_LARGE
	part_ids = np.random.choice(
	total_part_ids, num_k, replace=False
	).tolist()
	else:
	# Name the config
	cur_name = subset_str + "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
	total_part_ids = _PART_IDS_LARGE
	part_ids = total_part_ids[1 : num_k + 1]

	# Create configs
	BUILDER_CONFIGS.append(
	DiffusionDBConfig(
	name=cur_name,
	part_ids=part_ids,
	is_large=True,
	description=cur_description,
	),
	)

	# Need to manually add all (2m) and all (large)
	BUILDER_CONFIGS.append(
	DiffusionDBConfig(
	name="2m_all",
	part_ids=_PART_IDS,
	is_large=False,
	description="All images with their prompts and parameters",
	),
	)

	BUILDER_CONFIGS.append(
	DiffusionDBConfig(
	name="large_all",
	part_ids=_PART_IDS_LARGE,
	is_large=True,
	description="All images with their prompts and parameters",
	),
	)

	# We also prove a text-only option, which loads the meatadata parquet file
	BUILDER_CONFIGS.append(
	DiffusionDBConfig(
	name="2m_text_only",
	part_ids=[],
	is_large=False,
	description="Only include all prompts and parameters (no image)",
	),
	)

	BUILDER_CONFIGS.append(
	DiffusionDBConfig(
	name="large_text_only",
	part_ids=[],
	is_large=True,
	description="Only include all prompts and parameters (no image)",
	),
	)

	# Add a random 1k from 2M as the first entry point to show on HF data viewer
	# Sample part_ids
	part_ids = np.random.choice(_PART_IDS, 1000, replace=False).tolist()
	BUILDER_CONFIGS.append(
	DiffusionDBConfig(
	name="1k_random_2m",
	part_ids=part_ids,
	is_large=False,
	description="Another random 1k images with meta data from DiffusionDB 2M",
	),
	)

	# Default to only load 1k random images
	DEFAULT_CONFIG_NAME = "2m_random_1k"

	def _info(self):
	"""Specify the information of DiffusionDB."""

	if "text_only" in self.config.name:
	features = datasets.Features(
	{
	"image_name": datasets.Value("string"),
	"prompt": datasets.Value("string"),
	"part_id": datasets.Value("uint16"),
	"seed": datasets.Value("uint32"),
	"step": datasets.Value("uint16"),
	"cfg": datasets.Value("float32"),
	"sampler": datasets.Value("string"),
	"width": datasets.Value("uint16"),
	"height": datasets.Value("uint16"),
	"user_name": datasets.Value("string"),
	"timestamp": datasets.Value("timestamp[us, tz=UTC]"),
	"image_nsfw": datasets.Value("float32"),
	"prompt_nsfw": datasets.Value("float32"),
	},
	)

	else:
	features = datasets.Features(
	{
	"image": datasets.Image(),
	"prompt": datasets.Value("string"),
	"seed": datasets.Value("uint32"),
	"step": datasets.Value("uint16"),
	"cfg": datasets.Value("float32"),
	"sampler": datasets.Value("string"),
	"width": datasets.Value("uint16"),
	"height": datasets.Value("uint16"),
	"user_name": datasets.Value("string"),
	"timestamp": datasets.Value("timestamp[us, tz=UTC]"),
	"image_nsfw": datasets.Value("float32"),
	"prompt_nsfw": datasets.Value("float32"),
	},
	)

	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 = []

	# Resolve the urls
	if self.config.is_large:
	urls = _URLS_LARGE
	else:
	urls = _URLS

	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"))

	# Also download the metadata table
	metadata_path = dl_manager.download(urls["metadata"])

	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,
	"metadata_path": metadata_path,
	},
	),
	]

	def _generate_examples(self, data_dirs, json_paths, metadata_path):
	# 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.

	# Load the metadata parquet file if the config is text_only
	if "text_only" in self.config.name:
	metadata_df = pd.read_parquet(metadata_path)
	for _, row in metadata_df.iterrows():
	yield row["image_name"], {
	"image_name": row["image_name"],
	"prompt": row["prompt"],
	"part_id": row["part_id"],
	"seed": row["seed"],
	"step": row["step"],
	"cfg": row["cfg"],
	"sampler": _SAMPLER_DICT[int(row["sampler"])],
	"width": row["width"],
	"height": row["height"],
	"user_name": row["user_name"],
	"timestamp": None
	if pd.isnull(row["timestamp"])
	else row["timestamp"],
	"image_nsfw": row["image_nsfw"],
	"prompt_nsfw": row["prompt_nsfw"],
	}

	else:
	num_data_dirs = len(data_dirs)
	assert num_data_dirs == len(json_paths)

	# Read the metadata table (only rows with the needed part_ids)
	part_ids = []
	for path in json_paths:
	cur_id = int(re.sub(r"part-(\d+)\.json", r"\1", basename(path)))
	part_ids.append(cur_id)

	# We have to use pandas here to make the dataset preview work (it
	# uses streaming mode)
	metadata_table = pd.read_parquet(
	metadata_path,
	filters=[("part_id", "in", part_ids)],
	)

	# Iterate through all extracted zip folders for images
	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)

	# Query the metadata
	query_result = metadata_table.query(f'`image_name` == "{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"],
	"width": query_result["width"].to_list()[0],
	"height": query_result["height"].to_list()[0],
	"user_name": query_result["user_name"].to_list()[0],
	"timestamp": None
	if pd.isnull(query_result["timestamp"].to_list()[0])
	else query_result["timestamp"].to_list()[0],
	"image_nsfw": query_result["image_nsfw"].to_list()[0],
	"prompt_nsfw": query_result["prompt_nsfw"].to_list()[0],
	}