datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
prof_images_blip__22h-vintedois-diffusion-v0-1
Dataset Card for "prof_images_blip__22h-vintedois-diffusion-v0-1"
More Information needed
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.prof_images_blip__stabilityai-stable-diffusion-2
Dataset Card for "prof_images_blip__stabilityai-stable-diffusion-2"
More Information needed
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.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.Stable_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-datasetprof_images_blip__CompVis-stable-diffusion-v1-4
Dataset Card for "prof_images_blip__CompVis-stable-diffusion-v1-4"
More Information needed
diffusion_datasetDiffusionDream_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-upscaledegocentric-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
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.diffbir-restored-imageslaion1Maudio-diffusion-1024Over 20,000 256x256 mel spectrograms of 5 second samples of music from my Spotify liked playlist. The code to convert from audio to spectrogram and vice versa can be found in https://github.com/teticio/audio-diffusion along with scripts to train and run inference using De-noising Diffusion Probabilistic Models.
x_res = 1024
y_res = 1024
sample_rate = 44100
n_fft = 2048
hop_length = 512
1m2r-real-diffusion2This dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "kinova-arx",
"total_episodes": 72,
"total_frames": 32350,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 10,
"splits": {
"train": "0:72"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/rdoshi21/1m2r-real-diffusion2.diffusionpen-hebrew-handwriting
DiffusionPen Hebrew Handwriting
A large synthetic dataset of Hebrew handwritten text lines with ground-truth
transcriptions, for training and evaluating handwritten text recognition (HTR / OCR)
models. Every image is a single line of right-to-left Hebrew handwriting synthesized by
DiffusionPen — a style-conditioned latent-diffusion handwriting generator — in one of
491 distinct writer styles, and quality-filtered by an independent OCR pass.
149,952 line images, 491 writer… See the full description on the dataset page: https://huggingface.co/datasets/cyttic/diffusionpen-hebrew-handwriting.bm-diffusionprof_images_blip__wavymulder-Analog-Diffusion
Dataset Card for "prof_images_blip__wavymulder-Analog-Diffusion"
More Information needed
civitai-stable-diffusion-337k
How to Use
from datasets import load_dataset
dataset = load_dataset("thefcraft/civitai-stable-diffusion-337k")
print(dataset['train'][0])
download images
download zip files from images dir
https://huggingface.co/datasets/thefcraft/civitai-stable-diffusion-337k/tree/main/images
it contains some images with id
from zipfile import ZipFile
with ZipFile("filename.zip", 'r') as zObject: zObject.extractall()
Dataset Summary
GitHub URL:-… See the full description on the dataset page: https://huggingface.co/datasets/thefcraft/civitai-stable-diffusion-337k.