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iitolstykh/Bagel-NHR-Edit

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🥯 BAGEL-NHR-Edit

🌐 NHR Website | 📜 NHR Paper on arXiv | 🤗 NHR-Edit Dataset (part 1) | 🤗 NHR-Edit Dataset (part 2) |

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}
}