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
- DFloat11 Quantized Model Support: Support for DFloat11 quantized version that requires only ~22GB VRAM
Installation
1. Model Selection and Download
The ComfyUI-Bagel node now supports automatic model selection via dropdown:
- ByteDance-Seed/BAGEL-7B-MoT: Original standard model (~80GB VRAM recommended)
- DFloat11/BAGEL-7B-MoT-DF11: Quantized model (~22GB VRAM, single 24GB GPU compatible)
Models will be automatically downloaded to models/bagel/ when first selected. You can also manually download them:
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 DFloat11 quantized model support, also install:
pip install dfloat11
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 (1024x1024 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
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
The official recommendation for generating a 1024×1024 image is over 80GB GPU memory. However, multi-GPU setups can distribute the memory load. For example:
- Single GPU: A100 (40GB) takes approximately 340-380 seconds per image.
- Multi-GPU: 3 RTX3090 GPUs (24GB each) complete the task in about 1 minute.
- Compressed Model: Using the DFloat11 version requires only 22GB VRAM and can run on a single 24GB GPU, with peak memory usage around 21.76GB (A100) and generation time of approximately 58 seconds.
For more details, visit the GitHub issue.
2. Quantized Version
A quantized version of BAGEL is currently under development, which aims to reduce VRAM requirements further.
3. NameError: 'Qwen2Config' is not defined
This issue is likely related to environment or dependency problems. For more information, refer to this GitHub issue.