ComfyUI-OmniGen2

ComfyUI-OmniGen2

ComfyUI-OmniGen2 is now available in ComfyUI, OmniGen2 is a powerful and efficient unified multimodal model. Its architecture is composed of two key components: a 3B Vision-Language Model (VLM) and a 4B diffusion model.

Installation

1. Make sure you have ComfyUI installed

2. Clone this repository into your ComfyUI's custom_nodes directory:

cd ComfyUI/custom_nodes
git clone https://github.com/Yuan-ManX/ComfyUI-OmniGen2.git

Environment Setup

✅ Recommended Setup

# 1. Environment
cd ComfyUI-OmniGen2

# 2. (Optional) Create a clean Python environment
conda create -n omnigen2 python=3.11
conda activate omnigen2

# 3. Install dependencies
# 3.1 Install PyTorch (choose correct CUDA version)
pip install torch==2.6.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu124

# 3.2 Install other required packages
pip install -r requirements.txt

# Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4.
# Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue.
pip install flash-attn==2.7.4.post1 --no-build-isolation

🌏 For users in Mainland China

# Install PyTorch from a domestic mirror
pip install torch==2.6.0 torchvision --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu124

# Install other dependencies from Tsinghua mirror
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

# Note: Version 2.7.4.post1 is specified for compatibility with CUDA 12.4.
# Feel free to use a newer version if you use CUDA 12.6 or they fixed this compatibility issue.
pip install flash-attn==2.7.4.post1 --no-build-isolation -i https://pypi.tuna.tsinghua.edu.cn/simple

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Model

Download Pretrained Models

OmniGen2, a multimodal generation model, model weights can be accessed in huggingface and modelscope.

💡 Usage Tips

To achieve optimal results with OmniGen2, you can adjust the following key hyperparameters based on your specific use case.

• text_guidance_scale: Controls how strictly the output adheres to the text prompt (Classifier-Free Guidance).
• image_guidance_scale: This controls how much the final image should resemble the input reference image.
  • The Trade-off: A higher value makes the output more faithful to the reference image's structure and style, but it might ignore parts of your text prompt. A lower value (~1.5) gives the text prompt more influence.
  • Tip: For image editing task, we recommend to set it between 1.2 and 2.0; for in-context generateion task, a higher image_guidance_scale will maintian more details in input images, and we recommend to set it between 2.5 and 3.0.
• max_pixels: Automatically resizes images when their total pixel count (width × height) exceeds this limit, while maintaining its aspect ratio. This helps manage performance and memory usage.
  • Tip: Default value is 1024*1024. You can reduce this value if you encounter memory issues.
• max_input_image_side_length: Maximum side length for input images.
• negative_prompt: Tell the model what you don't want to see in the image.
  • Example: blurry, low quality, text, watermark
  • Tip: For the best results, try experimenting with different negative prompts. If you're not sure, just use the default negative prompt.
• enable_model_cpu_offload: Reduces VRAM usage by nearly 50% with a negligible impact on speed.
  • This is achieved by offloading the model weights to CPU RAM when they are not in use.
  • See: Model Offloading
• enable_sequential_cpu_offload: Minimizes VRAM usage to less than 3GB, but at the cost of significantly slower performance.
  • This works by offloading the model in submodules and loading them onto