Opening article
The Data is Better Together community has released an Apache 2.0 licensed dataset of text‑to‑image preference pairs, called open‑image‑preferences‑v1. The dataset includes 10,000 annotated pairs, each with simplified and complex prompts across 12 main categories such as Cinematic, Anime, and Digital art. Images were generated by two models—stabilityai/stable‑diffusion‑3.5‑large and black‑forest‑labs/FLUX.1‑dev—and annotated by over 250 community members, producing more than 30,000 responses in under two weeks. The dataset and accompanying code are available on the Hugging Face Hub and a GitHub repository, and a LoRA fine‑tuned FLUX‑dev adapter is also provided.
The Data is Better Together community releases yet another important dataset for open source development. Due to the lack of open preference datasets for text-to-image generation, we set out to release an Apache 2.0 licensed dataset for text-to-image generation. This dataset is focused on text-to-image preference pairs across common image generation categories, while mixing different model families and varying prompt complexities.
TL;DR? All results can be found in this collection on the Hugging Face Hub and code for pre- and post-processing can be found in this GitHub repository . Most importantly, there is a ready-to-go preference dataset and a flux-dev-lora-finetune . If you want to show your support already, don’t forget to like, subscribe and follow us before you continue reading further.
[Data is Better Together](https://huggingface.co/data-is-better-together) is a collaboration between 🤗 Hugging Face and the Open-Source AI community. We aim to empower the open-source community to build impactful datasets collectively. You can follow the organization to stay up to date with the latest datasets, models, and community sprints.
The ArtificialAnalysis/Text-to-Image-Leaderboard shows an overview of the best performing image models. We choose two of the best performing models based on their license and their availability on the Hub. Additionally, we made sure that the model would belong to different model families in order to not highlight generations across different categories. Therefore, we chose stabilityai/stable-diffusion-3.5-large and black-forest-labs/FLUX.1-dev . Each of these models was then used to generate an image for both the simplified and complex prompt within the same stylistic categories.
There have been several efforts to create an open image preference dataset but our effort is unique due to the varying complexity and categories of the prompts, alongside the openness of the dataset and the code to create it. The following are some of the efforts:
To get a proper input dataset for this sprint, we started with some base prompts, which we cleaned, filtered for toxicity and injected with categories and complexities using synthetic data generation with distilabel . Lastly, we used Flux and Stable Diffusion models to generate the images. This resulted in the open-image-preferences-v1 .
Imgsys is a generative image model arena hosted by fal.ai , where people provide prompts and get to choose between two model generations to provide a preference. Sadly, the generated images are not published publicly, however, the associated prompts are hosted on Hugging Face . These prompts represent real-life usage of image generation containing good examples focused on day-to-day generation, but this real-life usage also meant it contained duplicate and toxic prompts, hence we had to look at the data and do some filtering.
Open preference datasets enable more accurate fine‑tuning of text‑to‑image models, improving generation quality and aligning outputs with user preferences. They also foster community collaboration and transparency in model development.