datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Stable-Diffusion-Prompts
Stable Diffusion Dataset
This is a set of about 80,000 prompts filtered and extracted from the image finder for Stable Diffusion: "Lexica.art". It was a little difficult to extract the data, since the search engine still doesn't have a public API without being protected by cloudflare.
If you want to test the model with a demo, you can go to: "spaces/Gustavosta/MagicPrompt-Stable-Diffusion".
If you want to see the model, go to: "Gustavosta/MagicPrompt-Stable-Diffusion".
stable-diffusion-v1-5-glazed
Dataset Card for Stable Diffusion v1.5 Glazed Samples
Dataset Description
Dataset Summary
This dataset contains image samples originally generated by runwayml/stable-diffusion-v1-5
and subsequently processed by Glaze tool.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
[More Information Needed]
Dataset Structure
Data Instances
[More Information Needed]
Data Fields
[More Information… See the full description on the dataset page: https://huggingface.co/datasets/hanamizuki-ai/stable-diffusion-v1-5-glazed.diffusers-pr
Diffusers PR Dataset
Normalized snapshots of issues, pull requests, comments, reviews, and linkage data from huggingface/diffusers.
Files:
issues.parquet
pull_requests.parquet
comments.parquet
issue_comments.parquet (derived view of issue discussion comments)
pr_comments.parquet (derived view of pull request discussion comments)
reviews.parquet
pr_files.parquet
pr_diffs.parquet
review_comments.parquet
links.parquet
events.parquet
new_contributors.parquet… See the full description on the dataset page: https://huggingface.co/datasets/evalstate/diffusers-pr.diffusion-pretrain-set-ft1
diffusion-pretrain-set-ft1
A multi-source image-caption pretraining dataset assembled from ten upstream
sources via a uniform ingest pipeline. Designed for a full pretrain or finetune
pipeline meant to curate for any major diffusion model preliminary, with the sole
intent to create a more powerful baseline preliminary train and a baseline
for synthesizing images to train the next generation of the VLM model.
This is a lot like the snake eating it's own tail, so it must be… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/diffusion-pretrain-set-ft1.github-commits-diff-dedup-pjjs-april
Deduplicated Commits
Deduplicated based on diff:
content = '\n'.join(difflib.unified_diff(
old_content.splitlines(keepends=True),
new_content.splitlines(keepends=True),
n=5
))
Parameters:
Minimum ngram size: 5
MinHash ngram size: 5
MinHash threshold: 0.8
Diffusion4D-Animated-Raw
Diffusion4D Animated Assets
This dataset provides animated 3D assets referenced by Diffusion4D and
Objaverse-XL in a directly browsable format. The default split contains 67,988
rows. Each row includes metadata, a preview image, and a short preview video so
that assets can be inspected in the Hugging Face Data Studio without first
downloading the original 3D file.
The repository also mirrors available raw assets and keeps their original
source links and hashes. The current… See the full description on the dataset page: https://huggingface.co/datasets/DenisKochetov/Diffusion4D-Animated-Raw.math_difficulty_dataStable_Diffusion_3_RecaptionThis dataset is the one specified in the stable diffusion 3 paper which is composed of the ImageNet dataset and the CC12M dataset.
I used the ImageNet 2012 train/val data and captioned it as specified in the paper: "a photo of a 〈class name〉" (note all ids are 999,999,999)
CC12M is a dataset with 12 million images created in 2021. Unfortunately the downloader provided by Google has many broken links and the download takes forever.
However, some people in the community publicized the dataset.… See the full description on the dataset page: https://huggingface.co/datasets/gmongaras/Stable_Diffusion_3_Recaption.diffbir-mixed-setspokemon-gpt4-captions
Dataset Card for "pokemon-gpt4-captions"
This dataset is just lambdalabs/pokemon-blip-captions but the captions come from GPT-4 (Turbo).
Code used to generate the captions:
import base64
from io import BytesIO
import requests
from PIL import Image
def encode_image(image):
buffered = BytesIO()
image.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
returnimg_str.decode("utf-8")
def create_payload(image_string):
payload = {… See the full description on the dataset page: https://huggingface.co/datasets/diffusers/pokemon-gpt4-captions.diffusion_db_dedupe_from50k_train
Dataset Card for "diffusion_db_dedupe_from50k_train"
More Information needed
diffusion-pretrain-set-ft1-1024
diffusion-pretrain-set-ft1-1024
1024px (2x) upscale of AbstractPhil/diffusion-pretrain-set-ft1.
WARNING
MUCH OF THIS DATA WAS MODEL UPSCALED USING RAPID UPSCALERS.
THIS IS NOT CONSISTENTLY HIGH FIDELITY NOR IS IT EVEN CLOSE TO FAIR FIDELITY AT TIMES.
PLEASE use this ONLY for pretraining, new concepts, and simple design purposes ONLY. HEAVILY PRUNE FOR FINETUNING.
Thank you, good luck my friends.
Details
Model: realesr-general-x4v3 (SRVGG Compact… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/diffusion-pretrain-set-ft1-1024.diffpen-synth-datasetgithub-diffs-dedupeddapo14k_difficultyDiffusionDream_DatasetThis is the dataset of the diffusion dream dataset. The dataset contains the following columns:
info: A string describing the action taken in the frame
keyword: A string describing the keyword of the action
action: A string describing the action taken in the frame
current_frame: The current frame of the video
previous_frame_1: The frame before the current frame
previous_frame_2: The frame before the previous frame
previous_frame_3: The frame before the previous frame
previous_frame_4: The… See the full description on the dataset page: https://huggingface.co/datasets/fffffchopin/DiffusionDream_Dataset.instructpix2pix-clip-filtered-upscaledcommits-pjj-diff
Dataset Card for "commits-pjj-diff"
More Information needed
DFADD_MLAAD_DiffSSD_VoxCeleb2egocentric-kitchen-sample
Diffraction Egocentric Kitchen Capture Sample
A small, inspectable sample of human kitchen manipulation captured with Stray Scanner on a LiDAR-equipped iPhone: native RGB, metric depth and confidence, per-frame camera calibration, device odometry, raw device IMU, and explicitly estimated hand/object annotations.
Human observation sample. License: cc-by-4.0. This sample contains 3 recordings totaling 167.85 seconds. It is an observation dataset for evaluating human-video… See the full description on the dataset page: https://huggingface.co/datasets/diffracting/egocentric-kitchen-sample.stable-diffusion-prompts-stats-full-uncensoreddiffusiondb_2m_random_50k
Dataset Card for "diffusiondb_2m_random_50k"
More Information needed
diffusionprint_dataset
DiffusionPrint Patch Dataset
A dataset of 64x64 image patches for contrastive learning of diffusion-based inpainting forensics.
Each patch comes from either a real image or an AI-inpainted region generated by one of three diffusion models.
Columns
Column
Type
Description
image
bytes (PNG)
Lossless 64x64 RGB patch
master_index
int64
Row index in the original memmap archive
patch_path
string
Original relative path of the patch
category
string
real… See the full description on the dataset page: https://huggingface.co/datasets/giakoupg/diffusionprint_dataset.lexica-stable-diffusion-v1-5
Stable Diffusion Dataset
This is a set of about 80,000 Image-Prompt pairs generated by stable-diffusion-v1-5.
The Prompts come from dataset Stable-Diffusion-Prompts which filtered and extracted from the image finder for Stable Diffusion: "Lexica.art".
layout_diffusion_hypersimThis repository contains the data for SceneCraft: Layout-Guided 3D Scene Generation.
Project page: https://orangesodahub.github.io/SceneCraft
Code: https://github.com/OrangeSodahub/SceneCraft
DiffSpotDiffSpot: Can VLMs Spot Fine-Grained Visual Differences in Web Interfaces?
🤗 Dataset |
🐙 GitHub |
📄 arXiv
Vision-language models excel at high-level image–text alignment — but can they spot a subtle visual change? DiffSpot puts this to the test on rendered web interfaces, where a localized change is both a clean probe of fine-grained perception and a practical requirement for GUI agents and design tools. Each example is a pair of screenshots differing by a single mutated CSS… See the full description on the dataset page: https://huggingface.co/datasets/tencent/DiffSpot.ngld-grape-leaf-vlm-w-img-without-diff-ref-v6
cleaned
Dataset Source and Credits
This dataset is derived from the Niphad Grape Leaf Disease Dataset (NGLD) published on Mendeley Data.
Original dataset:
Title: Niphad Grape Leaf Disease Dataset (NGLD)
Authors: Madhuri Dharrao, Deepak Dharrao, Rakesh Sonawane
Institution: Symbiosis Institute of Technology, Symbiosis International University
DOI: https://doi.org/10.17632/8nnd2ypcv3.1
License: CC BY 4.0
The original dataset contains high-quality images of table grape… See the full description on the dataset page: https://huggingface.co/datasets/qingwuuu/ngld-grape-leaf-vlm-w-img-without-diff-ref-v6.diffusers-dependents
diffusers metrics
This dataset contains metrics about the huggingface/diffusers package.
Number of repositories in the dataset: 160
Number of packages in the dataset: 2
Package dependents
This contains the data available in the used-by
tab on GitHub.
Package & Repository star count
This section shows the package and repository star count, individually.
Package
Repository
There are 0 packages that have more than 1000 stars.
There are 3 repositories… See the full description on the dataset page: https://huggingface.co/datasets/open-source-metrics/diffusers-dependents.diffbir-restored-imagesegocentric-maintenance-sample
Diffraction Egocentric Maintenance Sample
Chest-mounted iPhone video of hands-on appliance maintenance: tape removal, brushing, panel handling and wiping recessed surfaces. 6 curated excerpts complement Diffraction's RGB-D kitchen sample with a different task domain.
This is human RGB observation data for evaluating video-language, temporal action understanding and hand/object interaction workflows. Depth, metric camera calibration/pose, IMU, robot commands and… See the full description on the dataset page: https://huggingface.co/datasets/diffracting/egocentric-maintenance-sample.