# 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, 2000]: 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, ), ) # 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"], }