Datasets:

Languages:
English
Multilinguality:
multilingual
Size Categories:
n>1T
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
xiaohk commited on
Commit
27978ee
1 Parent(s): e075e40

Add a new text_only config option

Browse files
Files changed (1) hide show
  1. diffusiondb.py +91 -35
diffusiondb.py CHANGED
@@ -3,8 +3,11 @@
3
  """Loading script for DiffusionDB."""
4
 
5
  import numpy as np
 
 
6
  from json import load, dump
7
  from os.path import join, basename
 
8
 
9
  import datasets
10
 
@@ -34,14 +37,20 @@ _LICENSE = "CC0 1.0"
34
  _VERSION = datasets.Version("0.9.0")
35
 
36
  # Programmatically generate the URLs for different parts
 
37
  # https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/images/part-000001.zip
38
  _URLS = {}
39
  _PART_IDS = range(1, 2001)
40
 
41
  for i in _PART_IDS:
42
- _URLS[
43
- i
44
- ] = f"https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/images/part-{i:06}.zip"
 
 
 
 
 
45
 
46
 
47
  class DiffusionDBConfig(datasets.BuilderConfig):
@@ -107,22 +116,46 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
107
  ),
108
  )
109
 
 
 
 
 
 
 
 
 
 
110
  # Default to only load 1k random images
111
  DEFAULT_CONFIG_NAME = "random_1k"
112
 
113
  def _info(self):
114
  """Specify the information of DiffusionDB."""
115
 
116
- features = datasets.Features(
117
- {
118
- "image": datasets.Image(),
119
- "prompt": datasets.Value("string"),
120
- "seed": datasets.Value("int64"),
121
- "step": datasets.Value("int64"),
122
- "cfg": datasets.Value("float32"),
123
- "sampler": datasets.Value("string"),
124
- },
125
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
126
  return datasets.DatasetInfo(
127
  description=_DESCRIPTION,
128
  features=features,
@@ -154,6 +187,11 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
154
  data_dirs.append(data_dir)
155
  json_paths.append(join(data_dir, f"part-{cur_part_id:06}.json"))
156
 
 
 
 
 
 
157
  return [
158
  datasets.SplitGenerator(
159
  name=datasets.Split.TRAIN,
@@ -171,26 +209,44 @@ class DiffusionDB(datasets.GeneratorBasedBuilder):
171
  # The `key` is for legacy reasons (tfds) and is not important in itself,
172
  # but must be unique for each example.
173
 
174
- # Iterate through all extracted zip folders
175
- num_data_dirs = len(data_dirs)
176
- assert num_data_dirs == len(json_paths)
177
-
178
- for k in range(num_data_dirs):
179
- cur_data_dir = data_dirs[k]
180
- cur_json_path = json_paths[k]
181
-
182
- json_data = load(open(cur_json_path, "r", encoding="utf8"))
183
-
184
- for img_name in json_data:
185
- img_params = json_data[img_name]
186
- img_path = join(cur_data_dir, img_name)
187
-
188
- # Yields examples as (key, example) tuples
189
- yield img_name, {
190
- "image": {"path": img_path, "bytes": open(img_path, "rb").read()},
191
- "prompt": img_params["p"],
192
- "seed": int(img_params["se"]),
193
- "step": int(img_params["st"]),
194
- "cfg": float(img_params["c"]),
195
- "sampler": img_params["sa"],
196
  }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3
  """Loading script for DiffusionDB."""
4
 
5
  import numpy as np
6
+ import pandas as pd
7
+
8
  from json import load, dump
9
  from os.path import join, basename
10
+ from huggingface_hub import hf_hub_url
11
 
12
  import datasets
13
 
 
37
  _VERSION = datasets.Version("0.9.0")
38
 
39
  # Programmatically generate the URLs for different parts
40
+ # hf_hub_url() provides a more flexible way to resolve the file URLs
41
  # https://huggingface.co/datasets/poloclub/diffusiondb/resolve/main/images/part-000001.zip
42
  _URLS = {}
43
  _PART_IDS = range(1, 2001)
44
 
45
  for i in _PART_IDS:
46
+ _URLS[i] = hf_hub_url(
47
+ "datasets/poloclub/diffusiondb", filename=f"images/part-{i:06}.zip"
48
+ )
49
+
50
+ # Add the metadata parquet URL as well
51
+ _URLS["metadata"] = hf_hub_url(
52
+ "datasets/poloclub/diffusiondb", filename=f"metadata.parquet"
53
+ )
54
 
55
 
56
  class DiffusionDBConfig(datasets.BuilderConfig):
 
116
  ),
117
  )
118
 
119
+ # We also prove a text-only option, which loads the meatadata parquet file
120
+ BUILDER_CONFIGS.append(
121
+ DiffusionDBConfig(
122
+ name="text_only",
123
+ part_ids=[],
124
+ description="Only include all prompts and parameters (no image)",
125
+ ),
126
+ )
127
+
128
  # Default to only load 1k random images
129
  DEFAULT_CONFIG_NAME = "random_1k"
130
 
131
  def _info(self):
132
  """Specify the information of DiffusionDB."""
133
 
134
+ if self.config.name == "text_only":
135
+ features = datasets.Features(
136
+ {
137
+ "image_name": datasets.Value("string"),
138
+ "prompt": datasets.Value("string"),
139
+ "part_id": datasets.Value("int64"),
140
+ "seed": datasets.Value("int64"),
141
+ "step": datasets.Value("int64"),
142
+ "cfg": datasets.Value("float32"),
143
+ "sampler": datasets.Value("string"),
144
+ },
145
+ )
146
+
147
+ else:
148
+ features = datasets.Features(
149
+ {
150
+ "image": datasets.Image(),
151
+ "prompt": datasets.Value("string"),
152
+ "seed": datasets.Value("int64"),
153
+ "step": datasets.Value("int64"),
154
+ "cfg": datasets.Value("float32"),
155
+ "sampler": datasets.Value("string"),
156
+ },
157
+ )
158
+
159
  return datasets.DatasetInfo(
160
  description=_DESCRIPTION,
161
  features=features,
 
187
  data_dirs.append(data_dir)
188
  json_paths.append(join(data_dir, f"part-{cur_part_id:06}.json"))
189
 
190
+ # If we are in text_only mode, we only need to download the parquet file
191
+ # For convenience, we save the parquet path in `data_dirs`
192
+ if self.config.name == "text_only":
193
+ data_dirs = [dl_manager.download(_URLS["metadata"])]
194
+
195
  return [
196
  datasets.SplitGenerator(
197
  name=datasets.Split.TRAIN,
 
209
  # The `key` is for legacy reasons (tfds) and is not important in itself,
210
  # but must be unique for each example.
211
 
212
+ # Load the metadata parquet file if the config is text_only
213
+ if self.config.name == "text_only":
214
+ metadata_df = pd.read_parquet(data_dirs[0])
215
+ for _, row in metadata_df.iterrows():
216
+ yield row["image_name"], {
217
+ "image_name": row["image_name"],
218
+ "prompt": row["prompt"],
219
+ "part_id": row["part_id"],
220
+ "seed": row["seed"],
221
+ "step": row["step"],
222
+ "cfg": row["cfg"],
223
+ "sampler": row["sampler"],
 
 
 
 
 
 
 
 
 
 
224
  }
225
+
226
+ else:
227
+ # Iterate through all extracted zip folders for images
228
+ num_data_dirs = len(data_dirs)
229
+ assert num_data_dirs == len(json_paths)
230
+
231
+ for k in range(num_data_dirs):
232
+ cur_data_dir = data_dirs[k]
233
+ cur_json_path = json_paths[k]
234
+
235
+ json_data = load(open(cur_json_path, "r", encoding="utf8"))
236
+
237
+ for img_name in json_data:
238
+ img_params = json_data[img_name]
239
+ img_path = join(cur_data_dir, img_name)
240
+
241
+ # Yields examples as (key, example) tuples
242
+ yield img_name, {
243
+ "image": {
244
+ "path": img_path,
245
+ "bytes": open(img_path, "rb").read(),
246
+ },
247
+ "prompt": img_params["p"],
248
+ "seed": int(img_params["se"]),
249
+ "step": int(img_params["st"]),
250
+ "cfg": float(img_params["c"]),
251
+ "sampler": img_params["sa"],
252
+ }