画像
OmniGen
OmniGen: one unified model for text-to-image, editing, subject-driven and identity-preserving generation
★ 4.3k スターMIT
ガイドを見る →Bagelは、ByteDance-Seedが公開しているオープンソースの画像プロジェクトです（GitHub: ByteDance-Seed/Bagel）。GitHubスターは6,191、フォークは544で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年5月4日です。
ByteDance's open-source unified multimodal model for understanding, generation and image editing.
Decide whether you need image understanding, text-to-image generation, image editing — or all three from one model.
Clone the repo and create the documented Conda environment with Python 3.10.
Install the requirements and flash-attention, and download the BAGEL-7B-MoT weights from Hugging Face.
Launch the Gradio interface and run your first understanding, generation or editing task.
One model covers visual understanding, text-to-image generation and image editing instead of separate specialist models.
The 7B MoT architecture routes understanding and generation through specialized transformer experts.
A ready Gradio app (also hosted as a Hugging Face Space) lets you try the model interactively.
Ships training guidance (TRAIN.md) and evaluation scripts for VLM, text-to-image and editing benchmarks.
Documented CFG controls for text and image guidance, denoising intervals and timestep shift let you balance fidelity and prompt adherence.
Dockerfiles and community ComfyUI and quantized (INT8/DF11-compressed) variants are linked from the README.
Clone BAGEL (note the repository directory name) and enter it.
git clone https://github.com/ByteDance-Seed/Bagel.git
cd BagelCreate the Conda environment and install dependencies including flash-attention.
conda create -n bagel python=3.10 -y
conda activate bagel
pip install -r requirements.txt
pip install flash_attn==2.5.8 --no-build-isolationDownload the BAGEL-7B-MoT weights from Hugging Face as the README directs, then start the Gradio WebUI to run inference.
python app.py # see README for the exact commandBAGEL is developed by ByteDance-Seed, ByteDance's Seed research team.
The code is Apache-2.0 licensed and the model weights are published on Hugging Face; check the model card for the weights' terms.
No — you download the weights and run inference locally on your own GPU.
Those focus mainly on understanding (or separate generation); BAGEL is designed as a single unified model for understanding, generation and editing, and the README publishes its benchmark comparisons.
Clone the repo, create the Conda env, install requirements and flash-attention, download the weights, and launch the Gradio WebUI.
OmniGen: one unified model for text-to-image, editing, subject-driven and identity-preserving generation
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