SD3.5 Large Blur

# Python 3.10+: pip install comfy-sdk==0.4.0
import os

from comfy_sdk import Comfy

api_key = os.environ.get("COMFY_API_KEY", "").strip()
if not api_key: raise RuntimeError("Set COMFY_API_KEY")
os.environ.setdefault("COMFY_BASE_URL", "https://cloud.comfy.org")
client = Comfy(api_key=api_key)
workflow = client.workflows.from_json({
    "3": {
        "class_type": "KSampler",
        "inputs": {
            "cfg": 4,
            "denoise": 1,
            "latent_image": [
                "33",
                0
            ],
            "model": [
                "4",
                0
            ],
            "negative": [
                "58",
                1
            ],
            "positive": [
                "58",
                0
            ],
            "sampler_name": "euler",
            "scheduler": "simple",
            "seed": 268264726798396,
            "steps": 30
        },
        "_meta": {
            "title": "KSampler"
        }
    },
    "4": {
        "class_type": "CheckpointLoaderSimple",
        "inputs": {
            "ckpt_name": "sd3.5_large_fp8_scaled.safetensors"
        },
        "_meta": {
            "title": "Load Checkpoint"
        }
    },
    "6": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "4",
                1
            ],
            "text": "crystal butterfly above the sea, white, hyper detailed, with diamonds"
        },
        "_meta": {
            "title": "CLIP Text Encode (Prompt)"
        }
    },
    "8": {
        "class_type": "VAEDecode",
        "inputs": {
            "samples": [
                "3",
                0
            ],
            "vae": [
                "4",
                2
            ]
        },
        "_meta": {
            "title": "VAE Decode"
        }
    },
    "9": {
        "class_type": "SaveImage",
        "inputs": {
            "filename_prefix": "ComfyUI",
            "images": [
                "8",
                0
            ]
        },
        "_meta": {
            "title": "Save Image"
        }
    },
    "33": {
        "class_type": "EmptySD3LatentImage",
        "inputs": {
            "batch_size": 1,
            "height": 1024,
            "width": 1024
        },
        "_meta": {
            "title": "EmptySD3LatentImage"
        }
    },
    "46": {
        "class_type": "ControlNetLoader",
        "inputs": {
            "control_net_name": "sd3.5_large_controlnet_blur.safetensors"
        },
        "_meta": {
            "title": "Load ControlNet Model"
        }
    },
    "50": {
        "class_type": "ConditioningZeroOut",
        "inputs": {
            "conditioning": [
                "6",
                0
            ]
        },
        "_meta": {
            "title": "Conditioning Zero Out"
        }
    },
    "57": {
        "class_type": "LoadImage",
        "inputs": {
            "image": "sd3.5_large_blur_input_image.png"
        },
        "_meta": {
            "title": "Load Image"
        }
    },
    "58": {
        "class_type": "ControlNetApplyAdvanced",
        "inputs": {
            "control_net": [
                "46",
                0
            ],
            "end_percent": 1,
            "image": [
                "57",
                0
            ],
            "negative": [
                "50",
                0
            ],
            "positive": [
                "6",
                0
            ],
            "start_percent": 0,
            "strength": 1,
            "vae": [
                "4",
                2
            ]
        },
        "_meta": {
            "title": "Apply ControlNet"
        }
    }
})

# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)