Vantage-DreamOmni2

By vantagewithaiView on GitHub →

ntage-DreamOmni2 brings Qwen2.5-VL’s multimodal intelligence to ComfyUI, enabling unified text–image understanding for both generation and editing. Create context-aware prompts, perform reference-based visual edits, and build adaptive image grids — all with high consistency, abstraction, and creative control.

How much VRAM does Vantage-DreamOmni2 require?

Direct Answer: The ComfyUI node Vantage-DreamOmni2 requires a minimum base VRAM of 256MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.

High (2-4GB)
Base VRAM:
256MB (0.3GB)
Recommended GPU:
4GB+ VRAM
Low VRAM Mode:
✓ Supported
Estimation Confidence:
HIGH

Interactive VRAM Compatibility Estimator

Estimated Total VRAM: 3.00 GBTarget: 8 GB
✅ Comfortable Fit

Your GPU has plenty of headroom. You can run this node safely with your active configurations!

Deploy on High-Performance GPUs

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Deploy on Cloud GPUs

Need more VRAM to run ComfyUI with this node? Rent low-cost, high-performance cloud GPUs on RunPod instantly.

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What Python packages are required for Vantage-DreamOmni2?

Direct Answer: Running Vantage-DreamOmni2 requires installing the following Python package dependencies: accelerate, bitsandbytes, einops, numpy, safetensors, sentencepiece. Ensure your ComfyUI environment has these packages active before launching.

requirements.txt
accelerate
bitsandbytes
einops
numpy
safetensors
sentencepiece

Interactive Setup & Dependency Resolver

Operating System:
Environment Type:
Run this terminal command in your ComfyUI root folder:
# Loading command...

Related Custom Nodes

The following custom nodes run in the same PyTorch / CUDA environment and share similar categories or workflows:

AITocha Vision
by aitocha128MB VRAM
AI_Text_Comfyui
by Zch6111256MB VRAM
AK XZ Axis (XY for any KSampler)
by akawana256MB VRAM
ArtyClaw Comfy Nodes
by artyclaw256MB VRAM

Frequently Asked Questions

How much VRAM does Vantage-DreamOmni2 require?

Vantage-DreamOmni2 requires a minimum of 256MB (0.3GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.

Can I run Vantage-DreamOmni2 on an RTX 3060, RTX 4070, or RTX 4090?

✅ RTX 3060 (12GB): Yes, fully compatible with 10.6GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.6GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.2GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.4GB headroom

What Python packages are required for Vantage-DreamOmni2?

To run Vantage-DreamOmni2, you need to install: accelerate, bitsandbytes, einops, numpy, safetensors, sentencepiece. You can install these using pip or add them to your requirements.txt file.

How can I reduce VRAM usage when running Vantage-DreamOmni2?

Vantage-DreamOmni2 supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.

How do I install Vantage-DreamOmni2 in ComfyUI?

To install Vantage-DreamOmni2: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/vantagewithai/Vantage-DreamOmni2, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.