Create diffusiondb-pixelart.py

diffusiondb-pixelart.py

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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/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)",
+        ),
+    )
+
+    # 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],
+                    }