datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
dna_rendering_processed
DNA-Rendering-Processed Dataset
Project Page | Paper | Code | Model
To enable Diffuman4D model training, we meticulously process the DNA-Rendering dataset by recalibrating camera parameters, optimizing image color correction matrices (CCMs), predicting foreground masks, and estimating human skeletons.
To promote future research in the field of human-centric 3D/4D generation, we have open-sourced our re-annotated labels for the DNA-Rendering dataset in this repo, which includes… See the full description on the dataset page: https://huggingface.co/datasets/krahets/dna_rendering_processed.objaverse-1.0-renderingsShapeNet_RenderingDiffusion-Reward-Modeling-for-Text-Rendering-Dataset
🖼️ Text-to-Image Rendering Dataset
A dataset of 14k text prompts for image generation with text rendering evaluation
📚 Dataset Overview
This dataset contains 14,000 text prompts specifically designed for:
Image generation with text rendering
Evaluating text preservation in generated images
Training diffusion models for better text rendering
Each prompt comes with:
Pre-extracted target text for rendering
5 Stable Diffusion 3 generated latents (70k total)
Dual… See the full description on the dataset page: https://huggingface.co/datasets/leffff/Diffusion-Reward-Modeling-for-Text-Rendering-Dataset.text_rendering
text_rendering
text_rendering
Trigger token: sks_textrender
Examples: 111
Format: p-image
Source: /Users/davidberenstein/Documents/programming/pruna/dataset-generator/training/text-rendering.zip
Use input.zip with p-image-trainer (Replicate). See TRAINING_PLAN.md in this directory.
Format
Trainer: p-image-trainer
Schema: See config.yml and TRAINING_PLAN.md in this repo.
Reproduce
generate.py in this repo documents how to regenerate this dataset… See the full description on the dataset page: https://huggingface.co/datasets/davidberenstein1957/text_rendering.korean-text-rendering-data
한글 텍스트 렌더링 학습 데이터
이미지 안에 정확한 한글 텍스트를 렌더링하는 능력 개선을 위해 만들어진 합성(synthetic) 이미지-프롬프트 데이터셋입니다. 2026년 5월~7월에 걸쳐 진행된 세 차례의 별도 학습 이터레이션에서 나온 데이터를 통합했습니다.
총 79,460장, 2개 config(콘텐츠 유형)로 구성. 각 config는 독립적으로 로드할 수 있습니다.
from datasets import load_dataset
ds = load_dataset("<repo_id>", name="diagram") # 유형별로 필요한 것만
이 릴리즈는 순수 한글 타이포그래피 학습에 초점을 맞춰 atomic_text(99.4% 한글)와
diagram(100% 한글) 두 유형만 포함합니다. 둘 다 코드·템플릿 기반 결정론적 생성이라
외부 생성형 서비스에 의존하지 않고, 라이선스 문제가 없습니다. "프롬프트 안 인용부호=정답
텍스트" 컨벤션은 둘 다… See the full description on the dataset page: https://huggingface.co/datasets/fasoo/korean-text-rendering-data.objaverse_rendering_setVoluspa_the_Seeresss_Vision_the_Ultimate_Poetic_Rendering
Völuspá the Seeress's Vision the Ultimate Poetic Rendering
Dataset Details
Dataset Description
This dataset weaves the ancient threads of Völuspá, the Seeress's profound vision from the Poetic Edda, into a modern ShareGPT JSONL format. It holds 66 sacred exchanges, each a rune-carved conversation: a mortal seeker requests the recitation of a stanza, and the divine voice responds with the poem's eternal words—from creation's dawn through Ragnarök's… See the full description on the dataset page: https://huggingface.co/datasets/RuneForgeAI/Voluspa_the_Seeresss_Vision_the_Ultimate_Poetic_Rendering.meaningfulness-cross-language-rendering
Cross-Language Rendering for Meaning vs Meaningfulness (Paper B 2026ap)
HF dataset DOI: 10.57967/hf/8971
Companion paper concept DOI: 10.5281/zenodo.20409701
Companion GitHub mirror: https://github.com/spectralbranding/meaningfulness-papers/tree/main/meaning-meaningfulness-empirical
Dataset Summary
This dataset contains the multi-language rendering and extraction artifacts demonstrating Proposition P4 (rendering-equivalence under spine-preservation) from Zharnikov… See the full description on the dataset page: https://huggingface.co/datasets/spectralbranding/meaningfulness-cross-language-rendering.qwen-image-text-renderingrendering_data