sft-simple

EARL - SFT (S) (8B)

Model Name: mair-lab/sft-simple
Model Size: 8B parameters
Base Model: BAAI/Emu3-Stage1
Training Method: Supervised Fine-Tuning (SFT)
Dataset: Simple Edit (S)

This model is part of the EARL benchmark effort introduced in our paper:
👉 EARL: The Promise of RL for Autoregressive Image Editing

Model Summary

This SFT model is fine-tuned from Emu3 using direct supervision on the Simple Edit dataset. It is optimized for general-purpose autoregressive image editing without requiring intermediate reasoning steps. This model achieves state-of-the-art performance on several editing benchmarks across modalities.

➡️ Inference script and usage: GitHub Repo

Benchmark Results (Avg Score Across Benchmarks)

📈 Note: The Emu3-based SFT (S) model achieves top results among all open-source supervised models on OmniEdit and competitive performance across other benchmarks.

Use Cases

• Open-ended and instruction-guided image editing
• Object, attribute, style and environment change