2.1 Creator - Diffusion Basics

# 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({
    "94": {
        "class_type": "ModelSamplingAuraFlow",
        "inputs": {
            "model": [
                "95",
                0
            ],
            "sampling": "flow",
            "shift": 3
        },
        "_meta": {
            "title": "ModelSamplingAuraFlow"
        }
    },
    "95": {
        "class_type": "UNETLoader",
        "inputs": {
            "unet_name": "z_image_turbo_bf16.safetensors",
            "weight_dtype": "default"
        },
        "_meta": {
            "title": "Load Diffusion Model"
        }
    },
    "96": {
        "class_type": "CLIPLoader",
        "inputs": {
            "clip_name": "qwen_3_4b.safetensors",
            "device": "default",
            "type": "lumina2"
        },
        "_meta": {
            "title": "Load CLIP"
        }
    },
    "97": {
        "class_type": "VAELoader",
        "inputs": {
            "vae_name": "ae.safetensors"
        },
        "_meta": {
            "title": "Load VAE"
        }
    },
    "98": {
        "class_type": "VAEDecode",
        "inputs": {
            "samples": [
                "102",
                0
            ],
            "vae": [
                "97",
                0
            ]
        },
        "_meta": {
            "title": "VAE Decode"
        }
    },
    "99": {
        "class_type": "ConditioningZeroOut",
        "inputs": {
            "conditioning": [
                "103",
                0
            ]
        },
        "_meta": {
            "title": "Conditioning Zero Out"
        }
    },
    "100": {
        "class_type": "SaveImage",
        "inputs": {
            "filename_prefix": "Z-Image-Turbo",
            "images": [
                "98",
                0
            ]
        },
        "_meta": {
            "title": "Save Image"
        }
    },
    "101": {
        "class_type": "EmptyLatentImage",
        "inputs": {
            "batch_size": 1,
            "height": 1920,
            "width": 1080
        },
        "_meta": {
            "title": "Empty Latent Image"
        }
    },
    "102": {
        "class_type": "KSampler",
        "inputs": {
            "cfg": 1,
            "denoise": 1,
            "latent_image": [
                "101",
                0
            ],
            "model": [
                "94",
                0
            ],
            "negative": [
                "99",
                0
            ],
            "positive": [
                "103",
                0
            ],
            "sampler_name": "res_multistep",
            "scheduler": "simple",
            "seed": 222678399973641,
            "steps": 8
        },
        "_meta": {
            "title": "KSampler"
        }
    },
    "103": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "96",
                0
            ],
            "text": "Editorial shot: On a volcanic black sand beach with a dark overcast sky.\n\nLow side-angle: Male model is in a deep squat, knees wide, head resting in one hand with a brooding, sharp gaze.\n\nHair: Ultra-short platinum blonde buzz cut.\n\nWardrobe: light-blue transparent oval sunglasses, oversized black wool chunky-knit sweater, baggy cargo trousers, climbing shoes. \n\nLighting: Theatrical cold blue moonlighting contrasted by a single sharp, warm orange spotlight on the face. Shot on Fujifilm GFX 100S, 110mm f/2 lens\n\nVibe: Paganism, futuristic tech-wear."
        },
        "_meta": {
            "title": "CLIP Text Encode (Prompt)"
        }
    }
})

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