// image · ByteDance-Seed/Bagel

Bagel

Bagel é um projeto de imagem de código aberto de ByteDance-Seed (ByteDance-Seed/Bagel no GitHub). Tem 6.191 estrelas no GitHub e 544 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 4 de maio de 2026.

ByteDance's open-source unified multimodal model for understanding, generation and image editing.

★ 6,2 mil estrelas⑂ 544 forksApache-2.0atualizado mai 2026

Ver no GitHub ↗

▦ visual: o README deste projeto não inclui nenhuma captura incorporável,
então aqui vai um marcador. O guia abaixo está completo mesmo assim.

// dados do projeto

Dados do projeto

Repositório: ByteDance-Seed/Bagel
Estrelas no GitHub: 6.191
Forks: 544
Licença: Apache-2.0
Última atualização no GitHub: 4 de maio de 2026
Categoria: Imagem
Tipo de ficha: Guia de instalação completo

// como funciona

Como funciona

Clarify your multimodal goal
Decide whether you need image understanding, text-to-image generation, image editing — or all three from one model.
Clone and create the environment
Clone the repo and create the documented Conda environment with Python 3.10.
Install dependencies and weights
Install the requirements and flash-attention, and download the BAGEL-7B-MoT weights from Hugging Face.
Run the WebUI
Launch the Gradio interface and run your first understanding, generation or editing task.

// recursos

Por que as pessoas gostam tanto

Unified multimodal model

One model covers visual understanding, text-to-image generation and image editing instead of separate specialist models.

Mixture-of-Transformers design

The 7B MoT architecture routes understanding and generation through specialized transformer experts.

Gradio WebUI included

A ready Gradio app (also hosted as a Hugging Face Space) lets you try the model interactively.

Training and evaluation scripts

Ships training guidance (TRAIN.md) and evaluation scripts for VLM, text-to-image and editing benchmarks.

Tunable generation controls

Documented CFG controls for text and image guidance, denoising intervals and timestep shift let you balance fidelity and prompt adherence.

Community deployment options

Dockerfiles and community ComfyUI and quantized (INT8/DF11-compressed) variants are linked from the README.

// execute você mesmo

Execute você mesmo

Clone the repository
Clone BAGEL (note the repository directory name) and enter it.
```
git clone https://github.com/ByteDance-Seed/Bagel.git
cd Bagel
```

Set up the environment

Create 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-isolation

Launch the WebUI
Download 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 command
```

// casos de uso

O que você pode criar

Researching unified understanding-and-generation architectures
Generating and then editing an image in one model session
Benchmarking an open multimodal model against hosted VLMs
Building an image-editing demo on the included Gradio app

// perguntas frequentes

Perguntas frequentes

Who maintains BAGEL?

BAGEL is developed by ByteDance-Seed, ByteDance's Seed research team.

Is BAGEL free?

The code is Apache-2.0 licensed and the model weights are published on Hugging Face; check the model card for the weights' terms.

Does it need a paid API key?

No — you download the weights and run inference locally on your own GPU.

How does it compare to Qwen2.5-VL or Janus-Pro?

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.

How do I get started?

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Baixe o Bagel

Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.

Ver no GitHub ↗← Mais ferramentas de Imagem