datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Image-Gen-or-Image-Editing
Image Gen or Image Editing
This dataset is designed for text classification of prompts provided by users. It determines whether a prompt is intended for image generation or image editing.
gpt-image-edit-benchmark-results
GPT-Image-Edit — Benchmark Results
This repository contains evaluation results of GPT-Image-Edit across four standard image-editing benchmarks. All scores were computed using the official evaluation scripts provided by each benchmark.
📊 Benchmarks
Benchmark
Metrics
Folder
GEdit-EN
12 editing categories + Avg
gedit/
Complex-Edit
IF, IP, PQ, Overall
complex_edit/
ImgEdit-Full
10 editing operations + Overall
imgedit/
OmniContext
Contextual edit scores… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/gpt-image-edit-benchmark-results.image-editing-model-notes
Image Editing Model Notes
Working notes on image-generation and prompt-based editing models.
I’m mainly interested in what happens after the first good-looking image:
whether the model follows small editing instructions
whether faces and expressions remain consistent
whether untouched objects quietly change
how well models handle text replacement
whether exact object counts are respected
how lighting edits affect skin and image texture
A visually strong result is not always a… See the full description on the dataset page: https://huggingface.co/datasets/drifterAI3000/image-editing-model-notes.image_edit