Flux.2 [Klein] 4B: Text to Image

# 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({
    "9": {
        "class_type": "SaveImage",
        "inputs": {
            "filename_prefix": "Flux2-Klein",
            "images": [
                "75:65",
                0
            ]
        },
        "_meta": {
            "title": "Save Image"
        }
    },
    "76": {
        "class_type": "PrimitiveStringMultiline",
        "inputs": {
            "value": "An expansive, sun-drenched conservatory perched high within a mist-shrouded redwood canopy during early dawn. In the center, a solitary hedgehog botanist wearing tiny brass spectacles meticulously trims glowing celestial orchids from a weathered wooden stepladder. Warm, dramatic morning sunbeams pierce diagonally through towering arched glass panes, casting sharp volumetric light across polished timber floorboards, scattered botanical parchment charts, and antique brass microscopes. Floating golden pollen and gentle morning haze drift through the air, creating a tranquil, reverent mood. Low-angle distant wide shot captured with a crisp 24mm prime lens, revealing the vast scale of the sunlit greenhouse surrounding the tiny diligent caretaker, with rich ambient warmth and soft atmospheric falloff."
        },
        "_meta": {
            "title": "Prompt"
        }
    },
    "75:61": {
        "class_type": "KSamplerSelect",
        "inputs": {
            "sampler_name": "euler"
        },
        "_meta": {
            "title": "KSamplerSelect"
        }
    },
    "75:62": {
        "class_type": "Flux2Scheduler",
        "inputs": {
            "height": [
                "75:69",
                0
            ],
            "steps": 20,
            "width": [
                "75:68",
                0
            ]
        },
        "_meta": {
            "title": "Flux2Scheduler"
        }
    },
    "75:63": {
        "class_type": "CFGGuider",
        "inputs": {
            "cfg": 5,
            "model": [
                "75:70",
                0
            ],
            "negative": [
                "75:67",
                0
            ],
            "positive": [
                "75:74",
                0
            ]
        },
        "_meta": {
            "title": "CFG Guider"
        }
    },
    "75:64": {
        "class_type": "SamplerCustomAdvanced",
        "inputs": {
            "guider": [
                "75:63",
                0
            ],
            "latent_image": [
                "75:66",
                0
            ],
            "noise": [
                "75:73",
                0
            ],
            "sampler": [
                "75:61",
                0
            ],
            "sigmas": [
                "75:62",
                0
            ]
        },
        "_meta": {
            "title": "SamplerCustomAdvanced"
        }
    },
    "75:65": {
        "class_type": "VAEDecode",
        "inputs": {
            "samples": [
                "75:64",
                0
            ],
            "vae": [
                "75:72",
                0
            ]
        },
        "_meta": {
            "title": "VAE Decode"
        }
    },
    "75:66": {
        "class_type": "EmptyFlux2LatentImage",
        "inputs": {
            "batch_size": 1,
            "height": [
                "75:69",
                0
            ],
            "width": [
                "75:68",
                0
            ]
        },
        "_meta": {
            "title": "Empty Flux 2 Latent"
        }
    },
    "75:67": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "75:71",
                0
            ],
            "text": ""
        },
        "_meta": {
            "title": "CLIP Text Encode (Negative Prompt)"
        }
    },
    "75:68": {
        "class_type": "PrimitiveInt",
        "inputs": {
            "value": 1344
        },
        "_meta": {
            "title": "Width"
        }
    },
    "75:69": {
        "class_type": "PrimitiveInt",
        "inputs": {
            "value": 768
        },
        "_meta": {
            "title": "Height"
        }
    },
    "75:70": {
        "class_type": "UNETLoader",
        "inputs": {
            "unet_name": "flux-2-klein-base-4b.safetensors",
            "weight_dtype": "default"
        },
        "_meta": {
            "title": "Load Diffusion Model"
        }
    },
    "75:71": {
        "class_type": "CLIPLoader",
        "inputs": {
            "clip_name": "qwen_3_4b.safetensors",
            "device": "default",
            "type": "flux2"
        },
        "_meta": {
            "title": "Load CLIP"
        }
    },
    "75:72": {
        "class_type": "VAELoader",
        "inputs": {
            "vae_name": "flux2-vae.safetensors"
        },
        "_meta": {
            "title": "Load VAE"
        }
    },
    "75:73": {
        "class_type": "RandomNoise",
        "inputs": {
            "noise_seed": 0
        },
        "_meta": {
            "title": "RandomNoise"
        }
    },
    "75:74": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "75:71",
                0
            ],
            "text": [
                "76",
                0
            ]
        },
        "_meta": {
            "title": "CLIP Text Encode (Positive Prompt)"
        }
    }
})

# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)
for output in job.get_outputs("9"):
    output.to_file(output.name)