Research map / Image generation / Editing and style transfer
RealEdit: Reddit Edits As a Large-scale Empirical Dataset for Image Transformations
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Introduces REALEDIT, a Reddit-sourced image editing dataset of real user requests and human edits (48K train, 9.3K test), and a model finetuned from InstructPix2Pix on it that beats baselines in human and VIEScore evaluations and helps deepfake/edit detection.
Problem
Existing image editing models and datasets rely on synthetic or artificial edits that do not reflect the diversity and distribution of real user editing requests.
Approach
Build a large-scale editing dataset by mining real user requests from Reddit (r/PhotoshopRequest, r/estoration) paired with human-made edits from comments, filter to InstructPix2Pix-compatible data, and finetune InstructPix2Pix with a Consistency Decoder at inference.
Key results
- On the REALEDIT test set, the model reaches a human Elo of 1184, 165 points above the next best model (AURORA at 1019).
- VIEScore of 3.68 overall versus 2.20 for the next best model (AURORA) on the REALEDIT test set.
- Finetuning a deepfake detector on REALEDIT raises F1 by 45.5 points on the REALEDIT test set and 14 points on TrueMedia.org in-the-wild data.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Fine-tune InstructPix2Pix on REALEDIT training examples | InstructPix2Pix · REALEDIT test set | VIE SC: 1.89 -> 4.61 | Helped |
| Fine-tune InstructPix2Pix on REALEDIT training examples | InstructPix2Pix · REALEDIT test set | Elo: 1019 -> 1184 | Helped |
| Train UFD from scratch with REALEDIT human-edited images | UFD · REALEDIT test set | F1: 23.5 -> 69; + 45.5 | Helped |
| Train UFD from scratch with REALEDIT human-edited images | UFD · REALEDIT test set | Precision: 80 -> 74; -6 | Hurt |
| Train UFD from scratch with REALEDIT human-edited images | UFD · TrueMedia.org in-the-wild dataset | F1: 49 -> 63; +14 | Helped |
| Train UFD from scratch with REALEDIT human-edited images | UFD · TrueMedia.org in-the-wild dataset | Precision: 80 -> 71; -9 | Hurt |
| Fine-tune InstructPix2Pix on REALEDIT training examples | InstructPix2Pix · GenAI Arena test set | Elo: 1107 -> 1043 | Hurt |
Surprising findings
- Copying the input image as the output scores highest on four of five standard automated metrics, showing these metrics fail to capture task completion.
- Synthetic datasets such as MagicBrush and Emu Edit differ strongly from real Reddit requests, with real requests more often about enhancement, people, and media.
Limitations
- Model is built on InstructPix2Pix only; other base models left for future work.
- Automated metrics like VIEScore and VQA-based scores are imperfect proxies for task completion.
- Training data filtered to exclude tasks incompatible with InstructPix2Pix, such as resizing, humor, and file-type changes.
Experimental setup
Datasets: REALEDIT MagicBrush Emu Edit GenAI Arena
Benchmarks: REALEDIT test set GenAI Arena Emu Edit MagicBrush TrueMedia.org in-the-wild
Metrics: VIEScore VQAScore TIFA L1 L2 CLIP-I DINO-I CLIP-T Elo F1
Baselines: InstructPix2Pix MagicBrush AURORA SDEdit HIVE Null-text Inversion
Builds on: InstructPix2Pix MagicBrush VIEScore Universal Fake Detect
Closest papers by meaning
- ImgEdit: A Unified Image Editing Dataset and Benchmark (NeurIPS 2025 · similarity 0.89)
- AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea (CVPR 2025 · similarity 0.87)
- UltraEdit: Instruction-based Fine-Grained Image Editing at Scale (NeurIPS 2024 · similarity 0.87)
- UnicEdit-10M: A Dataset and Benchmark Breaking the Scale-Quality Barrier via Unified Verification for Reasoning-Enriched Edits (CVPR 2026 · similarity 0.85)
- Learning Action and Reasoning-Centric Image Editing from Videos and Simulation (NeurIPS 2024 · similarity 0.85)
- HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks (AAAI 2024 · similarity 0.85)
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