Convert dataset to Parquet (#1)

- Convert dataset to Parquet (94ba78a0f5c1573c81dd6ba5327044938ea38007)
- Delete data file (47d9c3dd25e4e08499b1cef355a3bdf51ed1ce5b)
- Delete data file (7f4f46812f99df193a466bc3cbf9c7706fa3ea38)
- Delete loading script (fb866f9ef976198db28135808f739374cd940b7b)
- Delete data file (230d5ac618f5b2e73dc1c0df8e8599955daa7ecd)

README.md

@@ -0,0 +1,21 @@
+---
+dataset_info:
+  features:
+  - name: image
+    dtype: image
+  - name: conditioning_image
+    dtype: image
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 92595.0
+    num_examples: 10
+  download_size: 95645
+  dataset_size: 92595.0
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---

conditioning_images.zip → data/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6beda7f2f2f9e9cc965b49654b1c6304304f8fb951f89fc9d16b6a9c71c8598a
-size 25048
+oid sha256:0d0bfda6dcf271b69b81a2ae5c1538a16d0cf957ec0d9e630b368088c7ead520
+size 95645

fill10.py

@@ -1,101 +0,0 @@
-import pandas as pd
-from huggingface_hub import hf_hub_url
-import datasets
-import os
-
-_VERSION = datasets.Version("0.0.5")
-
-_DESCRIPTION = "TODO"
-_HOMEPAGE = "TODO"
-_LICENSE = "TODO"
-_CITATION = "TODO"
-
-_FEATURES = datasets.Features(
-    {
-        "image": datasets.Image(),
-        "conditioning_image": datasets.Image(),
-        "text": datasets.Value("string"),
-    },
-)
-
-METADATA_URL = hf_hub_url(
-    "hf-internal-testing/fill10",
-    filename="train.jsonl",
-    repo_type="dataset",
-)
-
-IMAGES_URL = hf_hub_url(
-    "hf-internal-testing/fill10",
-    filename="images.zip",
-    repo_type="dataset",
-)
-
-CONDITIONING_IMAGES_URL = hf_hub_url(
-    "hf-internal-testing/fill10",
-    filename="conditioning_images.zip",
-    repo_type="dataset",
-)
-
-_DEFAULT_CONFIG = datasets.BuilderConfig(name="default", version=_VERSION)
-
-
-class Fill50k(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [_DEFAULT_CONFIG]
-    DEFAULT_CONFIG_NAME = "default"
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=_FEATURES,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            license=_LICENSE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        metadata_path = dl_manager.download(METADATA_URL)
-        images_dir = dl_manager.download_and_extract(IMAGES_URL)
-        conditioning_images_dir = dl_manager.download_and_extract(
-            CONDITIONING_IMAGES_URL
-        )
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "metadata_path": metadata_path,
-                    "images_dir": images_dir,
-                    "conditioning_images_dir": conditioning_images_dir,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir):
-        metadata = pd.read_json(metadata_path, lines=True)
-
-        for _, row in metadata.iterrows():
-            text = row["text"]
-
-            image_path = row["image"]
-            image_path = os.path.join(images_dir, image_path)
-            image = open(image_path, "rb").read()
-
-            conditioning_image_path = row["conditioning_image"]
-            conditioning_image_path = os.path.join(
-                conditioning_images_dir, row["conditioning_image"]
-            )
-            conditioning_image = open(conditioning_image_path, "rb").read()
-
-            yield row["image"], {
-                "text": text,
-                "image": {
-                    "path": image_path,
-                    "bytes": image,
-                },
-                "conditioning_image": {
-                    "path": conditioning_image_path,
-                    "bytes": conditioning_image,
-                },
-            }

images.zip

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e671f5002ac91469cc282c9fa1ea41ccf4cd3d1a21596cd68360638e17993a9e
-size 21998

train.jsonl

@@ -1,10 +0,0 @@
-{"text": "light coral circle with white background", "image": "images/1.png", "conditioning_image": "conditioning_images/1.png"}
-{"text": "aqua circle with light pink background", "image": "images/2.png", "conditioning_image": "conditioning_images/2.png"}
-{"text": "cornflower blue circle with light golden rod yellow background", "image": "images/3.png", "conditioning_image": "conditioning_images/3.png"}
-{"text": "light slate gray circle with blue background", "image": "images/4.png", "conditioning_image": "conditioning_images/4.png"}
-{"text": "light golden rod yellow circle with turquoise background", "image": "images/5.png", "conditioning_image": "conditioning_images/5.png"}
-{"text": "crimson circle with papaya whip background", "image": "images/6.png", "conditioning_image": "conditioning_images/6.png"}
-{"text": "aqua circle with slate blue background", "image": "images/7.png", "conditioning_image": "conditioning_images/7.png"}
-{"text": "dark magenta circle with cyan background", "image": "images/8.png", "conditioning_image": "conditioning_images/8.png"}
-{"text": "papaya whip circle with corn silk background", "image": "images/9.png", "conditioning_image": "conditioning_images/9.png"}
-{"text": "silver circle with powder blue background", "image": "images/10.png", "conditioning_image": "conditioning_images/10.png"}