ComfyUI-Bagel

A ComfyUI custom node package based on the BAGEL-7B-MoT multimodal model.

About BAGEL

BAGEL is an open-source multimodal foundation model with 7B active parameters (14B total) that adopts a Mixture-of-Transformer-Experts (MoT) architecture. It is designed for multimodal understanding and generation tasks, outperforming top-tier open-source VLMs like Qwen2.5-VL and InternVL-2.5 on standard multimodal understanding leaderboards, and delivering text-to-image quality competitive with specialist generators such as SD3.

Features

Text-to-Image Generation: Generate high-quality images using natural language prompts

Image Editing: Edit existing images based on textual descriptions

Image Understanding: Perform Q&A and analysis on images

Reasoning Process Display: Optionally display the model’s reasoning process

Advanced Quantization Support: Multiple quantization modes (BF16, NF4, INT8) for the standard model

DFloat11 Quantized Model Support: Pre-quantized model requiring only ~22GB VRAM for single GPU setups

Installation

1. Model Selection and Download

The ComfyUI-Bagel node provides a model selection dropdown with automatic downloading capabilities:

Model Selection UI: Choose from available Hugging Face repository IDs or local folder names in the dropdown

Auto-download Feature: When allow_auto_download is enabled, models are automatically downloaded to models/bagel/ on first use

Local Model Detection: The node automatically scans for locally installed models in the models/bagel/ directory

Available Models:

ByteDance-Seed/BAGEL-7B-MoT: Standard model with multiple quantization options

BF16: Full precision mode (~80GB VRAM recommended for multi-GPU)

NF4: 4-bit quantization (~12-32GB VRAM, highly recommended for single GPU)

INT8: 8-bit quantization (~22-32GB VRAM, moderate compression)

DFloat11/BAGEL-7B-MoT-DF11: Pre-quantized model (~22GB VRAM, single 24GB GPU compatible)

[!IMPORTANT]

DFloat11 version do not need quantization again!

Memory is automatically calculated based on your GPU specifications and selected quantization mode – no manual configuration needed!

For manual installation, you can download models to models/bagel/:

Standard Model

# Clone model using git lfs (recommended)
git lfs install
git clone https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT models/bagel/BAGEL-7B-MoT

# Or use huggingface_hub
pip install huggingface_hub
python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='ByteDance-Seed/BAGEL-7B-MoT', local_dir='models/bagel/BAGEL-7B-MoT')"

DFloat11 Quantized Model (Recommended for single GPU)

# Clone DFloat11 quantized model
git clone https://huggingface.co/DFloat11/BAGEL-7B-MoT-DF11 models/bagel/BAGEL-7B-MoT-DF11

# Or use huggingface_hub
python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='DFloat11/BAGEL-7B-MoT-DF11', local_dir='models/bagel/BAGEL-7B-MoT-DF11')"

2. Install Dependencies

Install the required dependencies:

pip install -r requirements.txt

For advanced quantization support (NF4/INT8 modes), also install:

pip install bitsandbytes

For DFloat11 quantized model support, also install:

pip install dfloat11

Note: bitsandbytes is required for NF4 and INT8 quantization modes on the standard ByteDance model. DFloat11 model works without additional quantization libraries.

3. Restart ComfyUI

Restart ComfyUI to load the new nodes.

Workflows

Text-to-Image Generation

Generate high-quality images from text descriptions. Suitable for creative design and content generation.

Image Editing Workflow

Edit existing images based on textual descriptions, supporting local modifications and style adjustments.

Image Understanding Workflow

Analyze and answer questions about image content, suitable for content understanding and information extraction.

Performance Comparison

| Metric | BAGEL-7B-MoT (Standard Model) | BAGEL-7B-MoT (DFloat11 Quantized Model) |

|——–|——————————-|—————————————–|

| Model Size | 29.21 GB | 19.89 GB |

| Peak GPU Memory (1024×1024 image generation) | 30.07 GB | 21.76 GB |

| Generation Time (on an RTX4090 GPU) | 482.95 seconds | 154.39 seconds |

DFloat11 Quantized Model significantly reduces VRAM requirements and speeds up generation time, making it ideal for single GPU setups.

Related Links

BAGEL Official Paper

BAGEL Model Homepage

Hugging Face Model

Online Demo

Discord Community

License

This project is licensed under the Apache 2.0 License. Please refer to the official license terms for the use of the BAGEL model.

Contribution

Contributions are welcome! Please submit issue reports and feature requests. If you wish to contribute code, please create an issue to discuss your ideas first.

FAQ

1. VRAM Requirements & Optimization

With the new automatic memory management and quantization options:

24GB GPU (Single): Use NF4 quantization or DFloat11 model for optimal performance

12-16GB GPU: Use NF4 quantization for best results (may require some CPU offloading)

8GB GPU: Limited support with NF4 + aggressive offloading

Multi-GPU: BF16 mode automatically distributes load across available GPUs

Memory is calculated automatically based on your hardware – no manual configuration needed!

2. NameError: ‘Qwen2Config’ is not defined

This issue is likely related to environment or dependency problems. You can install flash-attention and triton with the following links:

v2.7.4.post1_crossos00 flash-attention-windows-wheel triton-windows-builds

For more information, refer to this GitHub issue.