datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
latent-sr-embeddings
Latent-SR Embeddings: Precomputed VAE Latents for Medical Image Super-Resolution
Precomputed VAE latent embeddings from the paper:
"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution"Sebastian Cajas, Ashaba Judith, Rahul Gorijavolu, Sahil Kapadia, Hillary Clinton Kasimbazi, Leo Kinyera, Emmanuel Paul Kwesiga, Sri Sri Jaithra Varma Manthena, Luis Filipe Nakayama, Ninsiima Doreen, Leo Anthony Celi.arXiv:2604.12152 (2026)… See the full description on the dataset page: https://huggingface.co/datasets/sebasmos/latent-sr-embeddings.voxcpm2-ghana-speech-ipa-latents
VoxCPM2 Ghana — Precomputed AudioVAE Latents
Precomputed VoxCPM-2 AudioVAE latents for a Ghanaian multilingual TTS fine-tune,
ready for training with the official train_voxcpm_finetune.py
(train_manifest: ghana-latents). No audio decoding or VAE encoding needed at train
time — the latent feat is fed straight to the VoxCPM-2 model with the IPA transcript.
Each language is a dataset subset:
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/ghanaopenai/voxcpm2-ghana-speech-ipa-latents.video-dit-latents-hq
Video DiT Latents - Animals (HQ)
Pre-computed VAE latents for training video generation models.
Dataset Info
Property
Value
Resolution
256×256 pixels
Latent Shape
(4, 16, 32, 32)
Frames
16 @ 8fps (2 seconds)
VAE
stabilityai/sd-vae-ft-mse
Classes
dog, cat, bird, horse, fish, lion, elephant, monkey, butterfly, deer
Usage
import torch
from pathlib import Path
# Load a single latent
latent = torch.load("dog/12345.pt")… See the full description on the dataset page: https://huggingface.co/datasets/Jnaranjo/video-dit-latents-hq.layergen-eval-latents
LayerGen — Eval-Set Latents (VAE latents + baked text embeddings)
Pre-encoded evaluation-set inputs for the LayerGen layer-decomposition / harmonization models,
so inference can run anywhere (off-AIP) without the raw video → VAE-encode → umT5-encode pipeline.
Each *.parquet is one clip and is fully self-contained:
column group
contents
{composite,mask,fg,bg}_latent_bytes (+ _shape, _dtype)
4-stream Wan-VAE latents, 81f/21 latent-T, fp16, [16,21,60,104]… See the full description on the dataset page: https://huggingface.co/datasets/cs-mshah/layergen-eval-latents.ego10k-vjepa-latents
Ego10k V-JEPA Latents Dataset
This dataset contains compressed, highly-informative Video Joint Embedding Predictive Architecture (V-JEPA) latents extracted from Ego-centric industrial manufacturing videos.
Dataset Structure
The dataset is partitioned into roughly 1GB .parquet chunks using PyArrow.
Data Source and Preprocessing
The latent embeddings in this dataset were systematically extracted from the Ego10k Master Dataset provided by build.ai. The… See the full description on the dataset page: https://huggingface.co/datasets/rookierufus/ego10k-vjepa-latents.voxcpm-ghana-latents
VoxCPM Ghana — Precomputed AudioVAE Latents
The exact training-ready data used to fine-tune
ghananlpcommunity/voxcpm-ghana:
precomputed VoxCPM-0.5B AudioVAE latents (16 kHz) for 42 Ghanaian languages +
filtered Ghanaian English, with language-tagged transcripts. Drop-in for
VoxCPM fine-tuning — no audio decoding or VAE encoding needed at train time.
1,756,157 clips · ~3,400 h · 16 kHz
42 Ghanaian languages (incl. Twi split: twi-asante, twi-akuapem) + en
AudioVAE from… See the full description on the dataset page: https://huggingface.co/datasets/ghananlpcommunity/voxcpm-ghana-latents.midashenglm-gen-training-latents
ModelsLab/midashenglm-gen-training-latents
Precomputed audio latents for fine-tuning
mispeech/midashenglm-gen,
paired with six-view prompts in the exact format the model was trained on.
This is not an audio dataset and not a caption dataset. Each record is the
output of the model's frozen DashengTokenizer encoder — 768-dimensional latents
at 25 Hz, stored float16 — next to the tagged prompt string built from the
source metadata.
Why it exists
The encoder is frozen… See the full description on the dataset page: https://huggingface.co/datasets/ModelsLab/midashenglm-gen-training-latents.latents-clap-v2danbooru2024-latents-sdxl-1ktar
Danbooru 2024 SDXL VAE latents in 1k tar
Dedicated dataset to align deepghs/danbooru2024-webp-4Mpixel. "4MP-Focus" for average raw image resolution.
Latents are ARB with maximum size of 1024x1024 as the recommended setting in kohyas. Major reason is to make sure I can finetune with RTX 3090. VRAM usage will raise drastically after 1024.
Generated from prepare_buckets_latents_v2.py, modified from prepare_buckets_latents.py.
Used for kohya-ss/sd-scripts. In theory it may replace… See the full description on the dataset page: https://huggingface.co/datasets/6DammK9/danbooru2024-latents-sdxl-1ktar.humanego_serve_bread_lingbot_lerobot_with_latents
HumanEgo Serve Bread LingBot LeRobot With Latents
This dataset contains LeRobot-format robot demonstrations for the task:
pick up the bread and place it on the plate
The repository has two standalone LeRobot-style roots:
humanego_serve_bread_lingbot_eef_train: 55 episodes, 41,603 frames, 55 videos.
humanego_serve_bread_lingbot_eef_val: 6 episodes, 5,533 frames, 6 videos.
Each split includes:
data/: episode parquet files.
videos/: MP4 videos for observation.images.ego_rgb.… See the full description on the dataset page: https://huggingface.co/datasets/Coffeecoderss/humanego_serve_bread_lingbot_lerobot_with_latents.sfx-latentsflux-schnell-teacher-latents
Flux Schnell Teacher Latents
Pre-computed latents, decoded images, and text embeddings from FLUX.1-schnell for distillation and research.
Usage
from datasets import load_dataset
# Load specific subset
ds = load_dataset("AbstractPhil/flux-schnell-teacher-latents", "train_512")
ds = load_dataset("AbstractPhil/flux-schnell-teacher-latents", "train_2_512")
ds = load_dataset("AbstractPhil/flux-schnell-teacher-latents", "train_3_512")
Subsets
Config… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/flux-schnell-teacher-latents.cache_vae_latents_train_20hq1lq-part-00imagenet1k_invae-latents_dinov2_pcaVJEPA-LATENTS-L2NORMldt-latents
1 Million Image Latents Toy Dataset
A lightweight toy dataset of 1 003 626 image latents paired with CLIP text embeddings.
Raw sources & extraction
LAION‑aesthetic (laion/laion2B-en-aesthetic):
Streamed via 🤗 datasets in 50 k-image blocks.
Filtered for aesthetic > 7.
Skipped PNG/CMYK or images < 32×32 px.
JourneyDB (MidJourney) (JourneyDB/JourneyDB):
Downloaded three zip archives per batch from Hugging Face.
Unzipped locally and selected the first 50 000 valid… See the full description on the dataset page: https://huggingface.co/datasets/shreenithi20/ldt-latents.libritts-r-mimi-latentsvoxcpm2-ghana-speech-ipa-latents
VoxCPM2 Ghana — Precomputed AudioVAE Latents
Precomputed VoxCPM-2 AudioVAE latents for a Ghanaian multilingual TTS fine-tune,
ready for training with the official train_voxcpm_finetune.py
(train_manifest: ghana-latents). No audio decoding or VAE encoding needed at train
time — the latent feat is fed straight to the VoxCPM-2 model with the IPA transcript.
Each language is a dataset subset:
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/ghananlpcommunity/voxcpm2-ghana-speech-ipa-latents.latentssit-latents-ode-heun-1000-class-0_1000-samples-segment-100-199ldt-latents-uint8capstone_sakuga_vae_latentslatents-clapgenai-bench-sdxl-latents
GenAI-Bench SDXL with Latent Trajectories
SDXL generations for the GenAI-Bench prompts, paired with
the per-step decoded-latent trajectory of each generation. This dataset was created to evaluate NoisyCLIP.
For every prompt, 10 images were generated (1600 prompts → 16,000 generations). Each generation provides:
the full-resolution final image,
the 50-step denoising trajectory (each step's latent decoded to a small preview image), and
a CLIP-FlanT5-XXL VQAScore measuring… See the full description on the dataset page: https://huggingface.co/datasets/asiimo/genai-bench-sdxl-latents.IN1k256-AR-buckets-latents_dc-ae-f32c32-sana-1.0Danbooru-Top1000-Latents-NPZyoruba-cfm-latentsLatentSkill
LatentSkill Data
This dataset repository contains the data released for LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents.
Code: https://github.com/yuaofan0-oss/LatentSkillPaper: https://arxiv.org/abs/2606.06087Checkpoint repository: https://huggingface.co/AofaYu71/LatentSkill
Contents
skill_pretrain/
train.jsonl
val.jsonl
skill_ift/
train.json
search_test/
2wikimultihopqa_test.jsonl
bamboogle_test.jsonl… See the full description on the dataset page: https://huggingface.co/datasets/AofaYu71/LatentSkill.voxcpm2-ghana-english-ipa-latents
VoxCPM2 Ghanaian English — Precomputed AudioVAE Latents
Precomputed VoxCPM-2 AudioVAE latents for a Ghanaian multilingual TTS fine-tune,
ready for training with the official train_voxcpm_finetune.py
(train_manifest: ghana-latents). No audio decoding or VAE encoding needed at train
time — the latent feat is fed straight to the VoxCPM-2 model with the IPA transcript.
Each language is a dataset subset:
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/ghanaopenai/voxcpm2-ghana-english-ipa-latents.Danbooru-Top1000-Latents-SDXL