Datasets:

opendiffusionai
/

pexels-woman-solo

Tasks:

Text-to-Image

Size:

1K<n<10K

Tags:

pexels

License:

Dataset card Files Files and versions Community

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train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 408
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 160, in _generate_tables
                  df = pandas_read_json(f)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1025, in read
                  obj = self._get_object_parser(self.data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1051, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1187, in parse
                  self._parse()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/json/_json.py", line 1403, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
              ValueError: Trailing data
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 233, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 163, in _generate_tables
                  raise e
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 137, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 408

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Overview

Around 8,000 hand-selected high-resolution 4k images of "a woman", suitable for both training, and "pre-training" of AI models. Images are all real-world realism based.

Background

I have been having difficulty training my early-stage txt2img model with GOOD, HIGH-RES images of what "a woman" is. Up until now, I have just been throwing a large number of random high-res images with "woman" in the auto-captioned details.

NOW, however, I have hand-selected a bunch of high-res (4k) images, with just "a woman" in it. (Well, okay, some have a dog in it, but mostly, "a woman" is the ONLY subject. No men, no crowds, no kids, no other women...)

Additionally, I have (tried to) thrown out all images with contents that I guess will be easily misconstrued by AI training. So, no "clever" photography shots, no super-wierd lighting, etc etc.

They are mostly just very simple shots.

Downloading the images

First install the pip based "txt2dataset" tool, and grab [https://huggingface.co/datasets/opendiffusionai/pexels-woman-solo/blob/main/woman-solo.jsonl](the jsonl file)

Then run

img2dataset --url_list woman-sharp.jsonl  --input_format "jsonl" \
--url_col "url" --output_format files \
--resize_mode no \
--output_folder pexels-woman-sharp --processes_count 8 --thread_count 8

.txt captions

As a temporary measure, you can steal pregenerated captions from one of the appropriate files in https://huggingface.co/datasets/opendiffusionai/pexels-photos-janpf

However, I am working on an even better set of captions for this subset of images. Stay tuned ...

Image license link

https://www.pexels.com/license/

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