🥯 BAGEL-NHR-Edit
This repository hosts the model weights for BAGEL, fine-tuned on the NHR-Edit dataset (on the part 1 only). For installation, usage instructions, and further documentation, please visit the official BAGEL GitHub repository.
🛠️ Training Setup
We performed parameter-efficient adaptation on the generation expert’s attention and FFN projection layers using LoRA.
LoRA parameters:
r = 16
lora_alpha = 16
dropout = 0.05
bias = "none"
target_modules = [
"v_proj_moe_gen",
"k_proj_moe_gen",
"mlp_moe_gen.down_proj",
"mlp_moe_gen.gate_proj",
"q_proj_moe_gen",
"mlp_moe_gen.up_proj",
"o_proj_moe_gen"
]
📊 Image Editing Metrics
Metrics for GEdit-Bench-EN:
| Model | GEdit-Bench-EN (SC) ↑ | GEdit-Bench-EN (PQ) ↑ | GEdit-Bench-EN (O) ↑ |
|---|---|---|---|
| BAGEL-7B-MoT | 7.983 | 6.570 | 6.921 |
| BAGEL-NHR-Edit | 8.067 | 6.881 | 7.115 |
Scoring model:
gpt-4.1-2025-04-14(with default temperature)
Metrics for ImgEdit-Bench:
| Model | Style | Extract | Remove | Background | Action | Adjust | Add | Replace | Compose | Overall ↑ |
|---|---|---|---|---|---|---|---|---|---|---|
| BAGEL-7B-MoT | 4.22 | 1.53 | 3.04 | 3.3 | 4.07 | 3.67 | 3.98 | 3.5 | 3.0 | 3.3 |
| BAGEL-NHR-Edit | 4.3 | 1.62 | 3.18 | 3.42 | 3.95 | 3.55 | 4.19 | 3.77 | 2.94 | 3.39 |
Scoring model:
gpt-4o-2024-11-20(with temperature = 0.0)
🖼️ Image Editing Results
Generated images for ImgEdit-Bench and GEdit-Bench are included in this repository.
Results comparison between original Bagel-7B-MoT and BAGEL-NHR-EDIT on samples from ImgEdit and GEdit benches:
License
BAGEL-NHR-Edit is licensed under the Apache 2.0 license. It is finetuned from ByteDance-Seed/BAGEL-7B-MoT, which is also licensed under Apache 2.0.
✍️ Citation
@article{Layer2025NoHumansRequired,
arxivId = {2507.14119},
author = {Maksim Kuprashevich and Grigorii Alekseenko and Irina Tolstykh and Georgii Fedorov and Bulat Suleimanov and Vladimir Dokholyan and Aleksandr Gordeev},
title = {{NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining}},
year = {2025},
eprint = {2507.14119},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2507.14119},
journal={arXiv preprint arXiv:2507.14119}
}