test-latent-diffusion-dataset.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://huggingface.co/datasets/dylanintech/test-latent-diffusion-dataset"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URLS = {
#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
# }

_URL="https://huggingface.co/datasets/dylanintech/test-latent-diffusion-dataset/resolve/main/lexica-pics-latent-diffusion-dataset-images.tar.gz?download=true"

descriptions = ['a new universe emerging from the big bang',
'A digital illustration of a dramatic and intense scene featuring a Viking ship at sea. The ship is intricately designed with Norse engravings and is being led by a fierce Viking warrior with glowing red eyes, wearing a helmet with horns. The ship sails on tumultuous sea waves under a dark, stormy sky. The warriors on the ship are equipped with shields and ready for battle. The color palette is dominated by a deep teal color, specifically using the hexadecimal color code #30b7b3 for the ocean waves and sky to create a cohesive and moody atmosphere. The style is gothic expressionism, with a sense of movement and aggression captured in the scene. The image is to be created in the style of Nvidia 3D Unreal Engine graphics, offering a hyper-realistic look with detailed textures and lighting effects.',
'Beautiful illustration by Herge, in the style of Tin Tin comics, of a man and woman sitting on a bench watching the sunset setting over the Golden Gate Bridge, golden hour, Beautiful stunning color scheme, masterpiece, intricate details.',
'Create a realistic old black and white photograph, historical documentary, in an open shot with a very wide angle from below of a gigantic, very tall ceiba tree that seems to reach the sky. This scene takes place in the dark night of an arid prehistoric desert where no plant sprouts grow, a desert that is only dust and stones with immense rocks.',
'distant view of a large round indigo temple in the center of a futuristic community. Extraterrestrial landscape. Planet SIRIUS. The moon and stars can be seen in the sky even during the day.',
'Acrylic paint in the style of Leonid Afremov, Angkor Wat, Cambodia',
'1970s dark fantasy book cover paper art of a blonde man with short hair in a grassy field looking at a starry sky, symmetric back view, Dungeons and Dragons style drawing',
'A high-tech space station located in the center of the country on a glacier with energy cannons. It includes a fire station, police station, hospital, train station, and military base.',
'a cute minimalistic simple capybara side profile, in the style of Jon Klassen, desaturated light and airy pastel color palette, nursery art, white background',
'Beautiful cozy, tiny, cramped bedroom with floor to ceiling glass windows overlooking a cyberpunk city at night, view from top of skyscraper, bookshelves, thunderstorm outside with torrential rain, detailed, high resolution, photorrealistic, dark, gloomy, moody aesthetic',
'Conceptual pencil drawing school of innovation of future architecture and AI',
'Create a vector pencil sketch of the futuristic and dystopian coastal seascape using colored pencils, inspired by impressionism.',
'Moon profile + Halftone pattern + editorial illustration of the memento morti + higly textured, genre defining mixed media collage painting + fringe absurdism + Award winning halftone pattern illustration + simple flowing shapes + subtle shadows + paper texture + minimalist color scheme + inspired by Zdzisław Beksiński.',
'detailed, vibrant illustration of a Tokyo neighborhood, by Herge, in the style of Tin-Tin comics, vibrant colors, detailed, lots of people, sunny day, attention to detail, 8k',
'Chaotic stunning New York City, skyline, illustrated by hergé, style of tin tin comics, pen and ink. vintage 90s anime style, black and white, colorful clouds, symmetric',
'detailed pen and ink illustration of a building in NYC, by Herge, in the style of tin-tin comics, vibrant colors, detailed, lots of people, sunny day, busy neighborhood',
'Illustration by Herge of a city at night where a Audrey Hepburn wears a black coat, city skyline, beautiful shading, black makeup, shiny glossy, dark night, (by Herge1.2)',
'Beached steampunk submarine shipwreck in swamp,  dark atmosphere, night, mijn, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, erte, 8 k',
'Pixel art with the dark empty space. In the middle, there is a small ice planet that still has vegetation and plants flowers.',
'Drawing rice field terrasses on mountains in the philippines. Line drawing, clean clear lines, coloring book style, bright colors, intricate details, elegant, discreete, serene, magical, peaceful, fine detail, pastel pencils, winsor McCay style.'
]


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class TestLatentDiffusionDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    # VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')

    #don't need this rn
    # BUILDER_CONFIGS = [
    #     datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
    # ]

    # DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        # if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
        #     features = datasets.Features(
        #         {
        #             "text": datasets.Value("string"),
        #             "image": datasets.Image()
        #             # These are the features of your dataset like images, labels ...
        #         }
        #     )
        # else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
        #     features = datasets.Features(
        #         {
        #             "sentence": datasets.Value("string"),
        #             "option2": datasets.Value("string"),
        #             "second_domain_answer": datasets.Value("string")
        #             # These are the features of your dataset like images, labels ...
        #         }
        #     )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "image": datasets.Image(),
                }
            ),  
            # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # 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
        # urls = _URLS[self.config.name]
        path = dl_manager.download(_URL)
        image_iters = dl_manager.iter_archive(path)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "images": image_iters
                },
            ),
        ]

    # # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    # def _generate_examples(self, filepath, split):
    #     # TODO: 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.
    #     with open(filepath, encoding="utf-8") as f:
    #         for key, row in enumerate(f):
    #             data = json.loads(row)
    #             if self.config.name == "first_domain":
    #                 # Yields examples as (key, example) tuples
    #                 yield key, {
    #                     "sentence": data["sentence"],
    #                     "option1": data["option1"],
    #                     "answer": "" if split == "test" else data["answer"],
    #                 }
    #             else:
    #                 yield key, {
    #                     "sentence": data["sentence"],
    #                     "option2": data["option2"],
    #                     "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
    #                 }

    def _generate_examples(self, images):
        idx = 0
        #iterate through the images
        for filepath, image in images:
            yield idx, {
                "image": {"path": filepath, "bytes": image.read()},
                "text": descriptions[idx]
            }
            idx += 1