ComfyUI Custom Node Development Skill

ComfyUI Custom Node Development

This skill helps you create custom ComfyUI nodes from Python code.

Quick Template

class MyNode:
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "image": ("IMAGE",),
                "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
            },
            "optional": {
                "mask": ("MASK",),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("output",)
    FUNCTION = "execute"
    CATEGORY = "Custom/MyNodes"

    def execute(self, image, value, mask=None):
        result = image * value
        return (result,)

NODE_CLASS_MAPPINGS = {"MyNode": MyNode}
NODE_DISPLAY_NAME_MAPPINGS = {"MyNode": "My Node"}

Converting Python to Node

When you have Python code to wrap:

Step 1: Identify inputs and outputs

# Original function
def apply_blur(image, radius=5):
    from PIL import ImageFilter
    return image.filter(ImageFilter.GaussianBlur(radius))

Step 2: Map types

| Python Type | ComfyUI Type | Conversion | |-------------|--------------|------------| | PIL Image | IMAGE | torch.from_numpy(np.array(pil) / 255.0) | | numpy array | IMAGE | torch.from_numpy(arr.astype(np.float32)) | | cv2 BGR | IMAGE | torch.from_numpy(cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0) | | float 0-255 | IMAGE | Divide by 255.0 | | Single image | Batch | tensor.unsqueeze(0) |

Step 3: Handle batch dimension

ComfyUI images are [B,H,W,C] - always process all batch items:

def execute(self, image, radius):
    batch_results = []
    for i in range(image.shape[0]):
        # Convert to PIL
        img_np = (image[i].cpu().numpy() * 255).astype(np.uint8)
        pil_img = Image.fromarray(img_np)

        # Your processing
        result = pil_img.filter(ImageFilter.GaussianBlur(radius))

        # Convert back
        result_np = np.array(result).astype(np.float32) / 255.0
        batch_results.append(torch.from_numpy(result_np))

    return (torch.stack(batch_results),)

Common Input Types

| Type | Shape/Format | Widget Options | |------|--------------|----------------| | IMAGE | [B,H,W,C] float 0-1 | - | | MASK | [H,W] or [B,H,W] float 0-1 | - | | LATENT | {"samples": [B,C,H,W]} | - | | MODEL | ModelPatcher | - | | CLIP | CLIP encoder | - | | VAE | VAE model | - | | CONDITIONING | [(cond, pooled), ...] | - | | INT | integer | default, min, max, step | | FLOAT | float | default, min, max, step, display | | STRING | str | default, multiline | | BOOLEAN | bool | default | | COMBO | str | List of options as type |

Checklist

[ ] INPUT_TYPES is a @classmethod
[ ] Return value is a tuple: return (result,)
[ ] Handle batch dimension [B,H,W,C]
[ ] Add to NODE_CLASS_MAPPINGS
[ ] Category uses / for submenus

References

NODE_TEMPLATE.md - Full template with V3 schema
OFFICIAL_DOCS.md - Official ComfyUI documentation
PURZ_EXAMPLES.md - Example nodes and workflows

Finding Similar Nodes

Use the MCP tools to find existing nodes for reference:

comfy_search("blur")        → Find blur implementations
comfy_spec("GaussianBlur")  → See how inputs are defined

Agent Skills: ComfyUI Custom Node Development

Install this agent skill to your local

Skill Files