language:
- en
base_model:
- mair-lab/sft-simple
EARL - RL Fine-tuned (S + C) (8B)
Model Name: mair-lab/sft-simple.rl-simple-n-complex
Model Size: 8B parameters
Base Checkpoint: mair-lab/sft-simple
Training Method: Supervised Fine-Tuning (SFT) on Simple Edits → Reinforcement Learning (RL) on Simple + Complex Edits
Datasets: Simple Edit (S), Complex Edit (C)
This model is part of the EARL benchmark study:
📄 EARL: The Promise of RL for Autoregressive Image Editing
Model Summary
This RL fine-tuned model builds on the SFT-simple checkpoint, using reinforcement learning to improve performance on both simple and complex edit tasks. It’s optimized using a human-aligned reward function across diverse editing instructions.
➡️ Inference instructions: GitHub Repo
Full Benchmark Results
| Model | Base Model | OmniEdit | EmuEdit | AURORA | MB | VisMin | I2EBench | AVG |
|---|---|---|---|---|---|---|---|---|
| Magicbrush | SD v1.5 | 3.43 | 3.28 | 3.01 | 3.64 | 3.48 | 3.06 | 3.32 |
| InstructPix2Pix | SD v1.5 | 3.97 | 3.24 | 3.05 | 3.12 | 2.94 | 3.23 | 3.26 |
| Aurora | SD v1.5 | 4.50 | 4.40 | 4.12 | 4.62 | 3.82 | 3.58 | 4.17 |
| Omnigen* | - | 5.68 | 5.00 | 4.10 | 4.68 | 4.09 | 4.68 | 4.70 |
| SFT (S) | Emu3 | 5.73 | 3.66 | 3.58 | 3.19 | 3.57 | 3.59 | 3.88 |
| EARL SFT (S) → RL (S+C) | SFT (S) | 6.39 | 4.47 | 4.27 | 4.52 | 4.93 | 4.19 | 4.80 |
🚀 Highlight: Our RL model outperforms all supervised and diffusion baselines, setting a new state-of-the-art across the EARL benchmark with 4.80 AVG.
Use Cases
- Simple edits of object, attribute, style and environment changes
- Complex edits of counting, spatial relation and action changes
- Instruction-following visual transformations