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# Original 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/jainr3/diffusiondb-pixelart/resolve/main/images/part-000001.zip
_URLS = {}
_PART_IDS = range(1, 2001)
for i in _PART_IDS:
_URLS[i] = hf_hub_url(
"jainr3/diffusiondb-pixelart",
filename=f"images/part-{i:06}.zip",
repo_type="dataset",
)
# Add the metadata parquet URL as well
_URLS["metadata"] = hf_hub_url(
"jainr3/diffusiondb-pixelart", filename="metadata.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, **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, 2]:
for sampling in ["first", "random"]:
num_k_str = f"{num_k}k" if num_k < 1000 else f"{num_k // 1000}m"
subset_str = "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
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
part_ids = total_part_ids[1 : num_k + 1]
# Create configs
BUILDER_CONFIGS.append(
DiffusionDBConfig(
name=cur_name,
part_ids=part_ids,
description=cur_description,
),
)
# Need to manually add all (2m)
BUILDER_CONFIGS.append(
DiffusionDBConfig(
name="2m_all",
part_ids=_PART_IDS,
description="All images with their prompts and parameters",
),
)
# We also prove a text-only option, which loads the metadata parquet file
BUILDER_CONFIGS.append(
DiffusionDBConfig(
name="2m_text_only",
part_ids=[],
description="Only include all prompts and parameters (no image)",
),
)
# 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
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)
print(metadata_path)
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],
}
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