datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sdxl-1.0
check sdxl.parrotzone.art for easy viewing ⋆｡°✩
all images were made with SDXL 1.0 + the 0.9 VAE
steps: 20
cfg scale: 7
no refiner
random seeds
SDXL-Generated-Stanford-Dogs
Dataset Card for Generated Dogs
10+ images for each class in the Stanford Dogs dataset, but all generated with SDXL. Images were filtered for CLIP score, and cartoonish images were removed.
This is a FiftyOne dataset with 1305 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/SDXL-Generated-Stanford-Dogs.Diverse-SDXL-Dogs
Dataset Card for Diverse-SDXL-Dogs
This is a FiftyOne dataset with 181 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/Diverse-SDXL-Dogs")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/Diverse-SDXL-Dogs.SDXL-Dogs
Dataset Card for SDXL Dogs
Images of dogs breeds in the Stanford Dogs dataset, generated by SDXL
This is a FiftyOne dataset with 191 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/SDXL-Dogs")
# Launch the App
session =… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/SDXL-Dogs.SDXL_ComfyUI_workflowsanimals_with_objects_sdxl
Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings
A Hugging Face Datasets repository accompanying the paper "Scendi Score: Prompt-Aware Diversity Evaluation via Schur Complement of CLIP Embeddings".
Code: https://github.com/aziksh-ospanov/scendi-score
Dataset Information
This dataset consists of images depicting various animals next to different objects, generated using SDXL. It is released as a companion to the research paper… See the full description on the dataset page: https://huggingface.co/datasets/aziksh/animals_with_objects_sdxl.sdxl_images_easy_prompts-artists-seed1sdxl-qwen-phase0
SDXL–Qwen Phase-0 dataset
Purpose-built training set for AbstractPhil/geolip-sdxl-aleph.
Each row pairs a Qwen-Image-Lightning render with the caption that produced it and an
encoder-invariant geometric "aleph" address derived from the caption's bytes. It exists to
retrain SDXL (which stays the base model) around a new text encoder (Qwen in place of
CLIP-G) under a rectified-flow objective: the render is the flow-matching target, and the
student learns to reproduce it from the… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/sdxl-qwen-phase0.sdxl-1024-100kimagenet-sdxl-quantized
ImageNet SDXL Quantized
This repository provides the ImageNet-1K dataset pre-encoded with the Stable Diffusion XL VAE encoder and quantized to uint8, allowing for faster training of latent diffusion models by eliminating the need for on-the-fly encoding.
Key Features
Reduces quantization error by 2dB PSNR compared to a linear encoding scheme
Provided in both 256 and 512 resolutions
Compatible with NumPy, JAX, and PyTorch
Usage
Loading the dataset… See the full description on the dataset page: https://huggingface.co/datasets/jon-kyl/imagenet-sdxl-quantized.bm-sdxlsyn_sdxlgenai-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.sdxl-turbo-sae-labels
SDXL-Turbo SAE Feature Labels
20,480 labeled sparse autoencoder features across 4 UNet attention blocks in SDXL-Turbo, plus 50K generated images with full activation logs.
Built for latent-dance — a real-time audio-reactive music visualizer using SAE steering at 50 FPS.
Important attribution: The SDXL-Turbo sparse autoencoders/checkpoints used here were trained and released by Surkov et al. / EPFL through sdxl-unbox. This dataset does not claim authorship of the SAE training. It… See the full description on the dataset page: https://huggingface.co/datasets/hammamiomar/sdxl-turbo-sae-labels.sdxl-turbo-rtmesdxl-artists
SDXL Artists
Completely offline version of https://sdxl.parrotzone.art
To use, simply clone and open index.html.
Original README as follows:
license: openrail++
check sdxl.parrotzone.art for easy viewing ⋆｡°✩
all images were made with SDXL 1.0 + the 0.9 VAE
steps: 20
cfg scale: 7
no refiner
random seeds
magufuli-SDxl-Comic-Style-OutputsVAEDecodedImages-SDXL
Dataset Card for Dataset Name
This dataset is a collection of pre/post SDXL VAE encoded-decoded pairs from the Danish newspaper TV2 Nord, based on alexandrainst/nordjylland-news-image-captioning.
Dataset Details
Dataset Description
Images are fed to diffusion models as latents - a distilled representation of the image that allows processing with reduced overhead. This is facilitated by a variational autoencoder (VAE), a neural network that encodes/decodes… See the full description on the dataset page: https://huggingface.co/datasets/joshuajewell/VAEDecodedImages-SDXL.sdxl_images_sb_prompts-multi_artist-seed1noun-attribute-sdxl-imagesfactorio-blueprint-visualizations-sdxl-lora-examples
Factorio Blueprint Visualizations SDXL Lora Examples
Examples of the usage of https://huggingface.co/piebro/factorio-blueprint-visualizations-sdxl-lora. The images are generated using 25 inference steps and a guidance_scale of 7. The filenames are composed like this: {counter}_{seed}_{prompt}.png.
SDXL_REGULARIZATION_IMAGESSDXL_REGULARIZATION_IMAGES
Dataset v1
Prompt: Beautiful girl
Negative Prompt: child
Resolution: (1024, 1024)
Base Model: sd_xl_base_1.0_0.9vae.safetensors, Refiner Model: sd_xl_refiner_1.0_0.9vae.safetensors
LoRA [sd_xl_offset_example-lora_1.0.safetensors] weight: 0.5
More Datasets will be added in future, Show your support by clicking like
parti-prompts-sdxl-1.0
Dataset Card for "parti-promtps-sdxl-1.0"
More Information needed
SDXL_Data_Curriculum_Step3SDXL_Data_Curriculum_Step2sdxl-datasetstyle-content-grid-SDXL
Style Content Grid SDXL
Dataset Structure
The dataset contains 1738 images of resolution 1024x1024, generated by Stable Diffusion XL (sd_xl_base_1.0 with model hash 31e35c80fc). They were all generated in lllyasviel/stable-diffusion-webui-forge,
with the following positive and negative prompts:
Positive prompt: <style> of a <content>, \n masterpiece, best quality, high quality,
Negative prompt: (worst quality, low quality, normal quality),
with the following… See the full description on the dataset page: https://huggingface.co/datasets/yuxi-liu-wired/style-content-grid-SDXL.bm-sdxlfake_sdxl_12kgeolip-sdxl-fid-scoring