datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
docs-imagesdiffusers-images-docs3dfront-render-diffusedog-exampleprof_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
DiffusionGS
[ICCV 2025] DiffusionGS: Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation and Reconstruction
Data Description
These are the demo results of our ICCV 2025 paper.
HuggingFace Model Link
We also release our models in HuggingFace:
https://huggingface.co/CaiYuanhao/DiffusionGS
Here are some video generation results demo:
· (a) Object-level Generation… See the full description on the dataset page: https://huggingface.co/datasets/CaiYuanhao/DiffusionGS.text2dataset
Text2Dataset
the dataset generate from ChatGPT's output and text2light for training the LoRA using in DiffusionLight
Code for training the LoRA can be found at DiffusionLight-LoRA-Trainer
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.stable-diffusion-webui
Stable Diffusion web UI
A browser interface based on Gradio library for Stable Diffusion.
Features
Detailed feature showcase with images:
Original txt2img and img2img modes
One click install and run script (but you still must install python and git)
Outpainting
Inpainting
Color Sketch
Prompt Matrix
Stable Diffusion Upscale
Attention, specify parts of text that the model should pay more attention to
a man in a ((tuxedo)) - will pay more attention to tuxedo
a man in a… See the full description on the dataset page: https://huggingface.co/datasets/PennyJX/stable-diffusion-webui.Diffusion4D-Animated-Mocap-7440
Diffusion4D Animated Mocap 7440
Rolling export of 7,440 technically and semantically selected animated assets.
Each asset has 12 yaw views (0..330 degrees, step 30), 24 sampled frames,
BVH motion, DINOv2 image embeddings, and one PNG preview. Batch archives are
uploaded only after local stage validation.
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.diffusers-examplestest-arraysStable_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.Diffusion-book-cn
《从零开始学扩散模型》
术语表
词汇
翻译
Corruption Process
退化过程
Pipeline
管线
Timestep
时间步
Scheduler
调度器
Gradient Accumulation
梯度累加
Fine-Tuning
微调
Guidance
引导
目录
第一部分 基础知识
第一章 扩散模型的原理、发展和应用
1.1 扩散模型的原理
1.2 扩散模型的发展
1.3 扩散模型的应用
第二章 HuggingFace介绍与环境准备
2.1 HuggingFace Space
2.2 Transformer 与 diffusers 库
2.3 环境准备… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFace-CN-community/Diffusion-book-cn.blip3o-256pokemon-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.diffbir-mixed-setsPongdiffusion_db_dedupe_from50k_train
Dataset Card for "diffusion_db_dedupe_from50k_train"
More Information needed
diffpen-synth-datasetdiffusion-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.image-relighting-diffusion-data
Learning Illumination Control in Diffusion Models — Dataset (HF)
Public data and evaluation assets for Learning Illumination Control in Diffusion Models (ReALM-GEN @ ICLR 2026).
Code
github.com/nishitanand/image-relighting-diffusion
Model weights
huggingface.co/nishitanand/sd-image-relighting-model
Paper
arxiv.org/abs/2604.24877
Project site
nishitanand.github.io/relighting-diffusion-website
Download (CLI)
Install the Hugging Face CLI… See the full description on the dataset page: https://huggingface.co/datasets/nishitanand/image-relighting-diffusion-data.prof_images_blip__CompVis-stable-diffusion-v1-4
Dataset Card for "prof_images_blip__CompVis-stable-diffusion-v1-4"
More Information needed
GenRef-wds
GenRef-1M
We provide 1M high-quality triplets of the form (flawed image, high-quality image, reflection) collected across
multiple domains using our scalable pipeline from [1]. We used this dataset to train our reflection tuning model.
To know the details of the dataset creation pipeline, please refer to Section 3.2 of [1].
Project Page: https://diffusion-cot.github.io/reflection2perfection
Dataset loading
We provide the dataset in the webdataset format for fast… See the full description on the dataset page: https://huggingface.co/datasets/diffusion-cot/GenRef-wds.diffusion_datasetimagenetDiffseg30k
🖼️ DiffSeg30k -- A multi-turn diffusion-editing dataset for localized AIGC detection
A dataset for segmenting diffusion-based edits — ideal for training and evaluating models that localize edited regions and identify the underlying diffusion model, as presented in the paper DiffSeg30k: A Multi-Turn Diffusion Editing Benchmark for Localized AIGC Detection.
📁 Dataset Usage
xxxxxxxx.image.png: Edited images. Each image may have undergone 1, 2, or 3 editing… See the full description on the dataset page: https://huggingface.co/datasets/Chaos2629/Diffseg30k.