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Exception: SplitsNotFoundError Message: The split names could not be parsed from the dataset config. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 153, in compute compute_split_names_from_info_response( File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 125, in compute_split_names_from_info_response config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config) File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 591, in get_previous_step_or_raise raise CachedArtifactError( libcommon.simple_cache.CachedArtifactError: The previous step failed. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info for split_generator in builder._split_generators( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators raise ValueError( ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response for split in get_dataset_split_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names info = get_dataset_config_info( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.
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Dataset Card for LFANIME
A dataset of anime frames collected by KaraKaraWitch.
Dataset Details
Dataset Description
LFANIME, or Low-Framerate Anime, comprises frames from Japanese animation. The dataset serves dual purposes—facilitating fine-tuning of image diffusion models and functioning as a pre-training resource. Moreover, we anticipate its utilization in image classification.
Important Note: LFAnime is not intended for watching anime. To discourage this application, we have intentionally lowered the frame rate and excluded audio from the dataset.
- Curated by: KaraKaraWitch
- Funded by [optional]: N/A
- Shared by [optional]: N/A
- Language(s) (NLP): Nil. Primarily japanese, but no audio is included.
- License: CC
Uses
A tar file compresses each "Episode," encompassing sequential anime frames. The dataset also incorporates chapters for episodes that have them. It's important to note that certain frame numbers may be absent intentionally.
Direct Use
We release this dataset for free in the hopes that it could be used for text to image generation and/or image classification.
Out-of-Scope Use
Technically speaking, this dataset could be used to watch anime. However we do not recommend as such.
Additionally there could be unforseen usage that the author does not intend.
Dataset Structure
Each tar file should generally follow this format LFAnime-[T(Test),A(Alpha),B(Beta),R(Release)]-[Sequential Index]-[AnilistID]-[Episode]
Each tar file should contain:
frame_[XXXX]_[detection_type]_[seconds (float)].jpg
kframes.log (scxvid keyframe log)
metadata.json (Selected frames + Detection metrics + Mode)
detection_type
can be one of the following:
- key (KeyFrame)
- p_key (Previous Frame from Key Frame)
- inter (Inter frame)
Dataset Creation
Curation Rationale
The emphasis has been on developing models for generating images from text, particularly in the realm of creating "anime"-style visuals.
Examples of such models include Waifu Diffusion and NovelAI's SD 1.x models. Regrettably, these models tend to converge, resulting in a consistent aesthetic.
While this aesthetic may appeal to many users, it poses a challenge when attempting to diverge from or fine-tune the ingrained visual style of most SD 1.x models.
Source Data
Data Collection and Processing
We've opted not to reveal the specific origins of the anime to establish a level of separation between the producers and this dataset.
Nevertheless, we can outline the processing steps as follows:
- Extract frames from the mkv file, sampling every 10 frames per second.
- Utilize scxvid to generate a timecode for identifying scene cuts.
- Exclude frames that precede or follow a scene cut (considering potential inclusion of 1/2 frames at each scene cut).
- Save the processed frames to a tar file.
Who are the source data producers?
We have decided not to disclose the exact sources.
Bias, Risks, and Limitations
As this dataset is a personal collection from KaraKaraWitch, it will have tendencies to generally not "Shonen" anime and will have female protagonists in general.
Recommendations
Users should be made aware of the risks, biases and limitations of the dataset.
Citation [optional]
@misc{lfanime,
title = {LFAnime: A Low Framerate anime dataset.},
author = {KaraKaraWitch},
year = {2023},
howpublished = {\url{https://huggingface.co/datasets/RyokoExtra/LFANIME}},
}
Glossary [optional]
Anime:
Anime (Japanese: アニメ, IPA: [aꜜɲime]) is hand-drawn and computer-generated animation originating from Japan. Outside Japan and in English, anime refers specifically to animation produced in Japan.[1] However, in Japan and in Japanese, anime (a term derived from a shortening of the English word animation) describes all animated works, regardless of style or origin. Many works of animation with a similar style to Japanese animation are also produced outside Japan. Video games sometimes also feature themes and artstyles that can be considered as "anime".
- Wikipedia
Contributions
- @KaraKaraWitch (Twitter) for gathering this dataset.
- ChatGPT rewording sentences in this datacard.
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