Add a loading script

diffusiondb.py

@@ -0,0 +1,187 @@
+# 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"],
+                }