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1. Linux Environment Deployment of ComfyUI#

Environment Preparation#

• Python 3.10 or higher
• CUDA-supported GPU (recommended)
• Git

Installation Steps#

# Clone the repository
git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI

# Create a virtual environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

Start the Service#

python main.py

2. Workflow Development Guide#

Basic Concepts#

ComfyUI adopts a node-based programming approach, where each node represents a functional module, and workflows are built by connecting different nodes.

Custom Node Development#

# custom_node.py
class CustomNode:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",),
                "strength": ("FLOAT", {
                    "default": 1.0,
                    "min": 0.0,
                    "max": 1.0,
                    "step": 0.01
                }),
            },
        }
    
    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "process"
    
    def process(self, image, strength):
        # Implement your image processing logic
        return (processed_image,)

Workflow Example#

Here is a basic image generation workflow:

1. Load model node
2. Text prompt node
3. KSampler node
4. Image save node

3. Competitor Comparison Analysis#

Comparison with Stable Diffusion WebUI#

Advantages#

• More flexible workflow design
• Better version control support
• Greater performance optimization potential
• Easier process automation

Disadvantages#

• Higher entry barrier
• Interface is not as intuitive as WebUI
• Relatively smaller community

4. Best Practice Recommendations#

• Organize node layout reasonably to keep workflows clear
• Make good use of composite node functions to improve reuse efficiency
• Regularly back up important workflows
• Build a personal node library to enhance development efficiency

With the above content, you should be able to successfully deploy ComfyUI in a Linux environment, develop custom features, and have a clear understanding of its differences from other similar tools. As you delve deeper into practice, you will find that ComfyUI's powerful extensibility and flexibility can meet various complex AI image processing needs.