datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
diffusers-imagescommunity-pipelines-mirror
Community Pipeline Examples
For more information about community pipelines, please have a look at this issue.
Community pipeline examples consist pipelines that have been added by the community.
Please have a look at the following tables to get an overview of all community examples. Click on the Code Example to get a copy-and-paste ready code example that you can try out.
If a community pipeline doesn't work as expected, please open an issue and ping the author on it.
Please… See the full description on the dataset page: https://huggingface.co/datasets/diffusers/community-pipelines-mirror.docs-imagesdiffusers-images-docsdog-examplediffusers-pr
Diffusers PR Dataset
Normalized snapshots of issues, pull requests, comments, reviews, and linkage data from huggingface/diffusers.
Files:
issues.parquet
pull_requests.parquet
comments.parquet
issue_comments.parquet (derived view of issue discussion comments)
pr_comments.parquet (derived view of pull request discussion comments)
reviews.parquet
pr_files.parquet
pr_diffs.parquet
review_comments.parquet
links.parquet
events.parquet
new_contributors.parquet… See the full description on the dataset page: https://huggingface.co/datasets/evalstate/diffusers-pr.diffusers-examplesdiffusers-metadatatest-arrayspokemon-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.diffusers-testingdiffusers-dependents
diffusers metrics
This dataset contains metrics about the huggingface/diffusers package.
Number of repositories in the dataset: 160
Number of packages in the dataset: 2
Package dependents
This contains the data available in the used-by
tab on GitHub.
Package & Repository star count
This section shows the package and repository star count, individually.
Package
Repository
There are 0 packages that have more than 1000 stars.
There are 3 repositories… See the full description on the dataset page: https://huggingface.co/datasets/open-source-metrics/diffusers-dependents.instructpix2pix-clip-filtered-upscaledmvp-certificatesdiffusers-blackwell-quantsOutput artifacts for https://github.com/sayakpaul/diffusers-blackwell-quants
diffusers_readme_imagesmodular-diffusers-blogeye_rolling
Eye Rolling Image-video dataset
containing pairs of face images with corresponding video of the person rolling their eyes
images were downloaded from Unsplash
videos were created using LivePortrait
diffusers-qa-chatbot-artifactstorch-profiling-trace-diffusersbenchmarks
Welcome to 🤗 Diffusers Benchmarks!
This is dataset where we keep track of the inference latency and memory information of the core models in the diffusers library.
Currently, the core models are:
Flux
Wan
LTX
SDXL
Note that we will continue to extend this list based on their usage.
You can analyze the results in this demo.
[!IMPORTANT]
Instead of benchmarking the entire diffusion pipelines, we only benchmark the forward passes
of the diffusion networks under different settings… See the full description on the dataset page: https://huggingface.co/datasets/diffusers/benchmarks.cat_toy_exampleShotDEAD-v0
ShotDEAD-v0
Shot Directors Environment Actors Dataset
This dataset covers environment and contains still frames from a variety of films. The tags describe visual attributes of each image, including color, lighting, and composition.
Dataset Structure
Example Tags
Each image is labeled with the following categories:
COLOR
Indicates the dominant color palette in the image:
Mixed
Saturated
Desaturated
Warm
Red
Blue
Cyan
LIGHTING
Describes… See the full description on the dataset page: https://huggingface.co/datasets/diffusers/ShotDEAD-v0.tuxemonTuxemon Dataset
This dataset contains images of mosnters from The Tuxemon Project - an open source effort for a monster catching game.
These image-caption pairs can be used for text-to-image tuning & benchmarking.
All images in this dataset were downloaded from https://wiki.tuxemon.org/Category:Monster
Some images were upscaled using SDx4 upscaler & HiDiffusion
Captions generated with
BLIP-large (some were manually modified)
GPT-4 Turbo
[!TIP]
One can use the mix of captions provided in the… See the full description on the dataset page: https://huggingface.co/datasets/diffusers/tuxemon.diffusers-gallery-datatorchao-diffuserssdxl-1.0
Dataset Card for "sdxl-1.0"
Dataset was generated using the code below:
import torch
from datasets import Dataset, Features
from datasets import Image as ImageFeature
from datasets import Value, load_dataset
from diffusers import DDIMScheduler, DiffusionPipeline
import PIL
def main():
print("Loading dataset...")
parti_prompts = load_dataset("nateraw/parti-prompts", split="train")
print("Loading pipeline...")
ckpt_id = "stabilityai/stable-diffusion-xl-base-1.0"… See the full description on the dataset page: https://huggingface.co/datasets/diffusers-parti-prompts/sdxl-1.0.test-slicestwodgirl_diffusers_to_flux_scriptThis script is from https://huggingface.co/twodgirl/flux-devpro-schnell-merge-fp8-e4m3fn-diffusers.
The author is twodgirl.
Flux D+S F8 Diffusers
A + B merge, meant for gradio inference, possibly build adapters on top with diffusers.
Inference
Instruction
This model needs 10 steps, otherwise the images remain blurry.
ComfyUI
I convert all my checkpoints locally. There are 5+ file formats out there, they take up too much space.
Download the contents of this… See the full description on the dataset page: https://huggingface.co/datasets/John6666/twodgirl_diffusers_to_flux_script.diffusers-community-pipelines-mirror