datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sdxl-models
Aisha-AI.com 💜
A NSFW Social Network powered by AI Characters
The models saved in this dataset are currently being used, or have been used at some point, to generate images and videos.
The dataset is public and can be used as a backup or alternative to more unstable servers (like the unfortunate Civitai).
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-lorassdxl-pony-models-backupsdxl-backup-202412sdxl-1-0-models-backupsdxl_10_reg
Stable Diffusion XL 1.0 Regularization Images
Note: All of these images were generated without the refiner. These are sdxl 1.0 base only.
This is some of my SDXL 1.0 regularization images generated with various prompts that are useful for regularization images or other specialized training. (color augmentation, bluring, shapening, etc). I will attempt to add more as I go along with various categories.
Each image has a corrisponding txt file with the prompt used to generate it as… See the full description on the dataset page: https://huggingface.co/datasets/ostris/sdxl_10_reg.animals_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-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_workflowssa1b-sdxl-latents-1024sdxl_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_Sliderssdxl_images_mj_prompts-artists-seed7danbooru2024-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.sdxl_images_mj_prompts-artists-seed3sdxl-1024-100ksdxl_images_mj_prompts-artists-seed5sd-sdxl-lorasanhaLora-SDXLimagenet-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.sdxl_images_sb_prompts-single_artist-seed0imagenet-sdxl-vae-uint8sdxl_images_mj_prompts-artists-seed6sdxl-model-eciyuansyn_sdxlloras_sdxl