ChronoEdit 14B

# 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({
    "52": {
        "class_type": "LoadImage",
        "inputs": {
            "image": "image_chrono_edit_input_image.png"
        },
        "_meta": {
            "title": "Load Image"
        }
    },
    "60": {
        "class_type": "SaveImage",
        "inputs": {
            "filename_prefix": "Chrono_Edit_14B",
            "images": [
                "97:59",
                0
            ]
        },
        "_meta": {
            "title": "Save Image"
        }
    },
    "97:3": {
        "class_type": "KSampler",
        "inputs": {
            "cfg": 4,
            "denoise": 1,
            "latent_image": [
                "97:50",
                2
            ],
            "model": [
                "97:58",
                0
            ],
            "negative": [
                "97:50",
                1
            ],
            "positive": [
                "97:50",
                0
            ],
            "sampler_name": "uni_pc",
            "scheduler": "simple",
            "seed": 859991172109574,
            "steps": 20
        },
        "_meta": {
            "title": "KSampler"
        }
    },
    "97:37": {
        "class_type": "UNETLoader",
        "inputs": {
            "unet_name": "chrono_edit_14B_fp16.safetensors",
            "weight_dtype": "fp8_e4m3fn"
        },
        "_meta": {
            "title": "Load Diffusion Model"
        }
    },
    "97:38": {
        "class_type": "CLIPLoader",
        "inputs": {
            "clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
            "device": "default",
            "type": "wan"
        },
        "_meta": {
            "title": "Load CLIP"
        }
    },
    "97:39": {
        "class_type": "VAELoader",
        "inputs": {
            "vae_name": "wan_2.1_vae.safetensors"
        },
        "_meta": {
            "title": "Load VAE"
        }
    },
    "97:49": {
        "class_type": "CLIPVisionLoader",
        "inputs": {
            "clip_name": "clip_vision_h.safetensors"
        },
        "_meta": {
            "title": "Load CLIP Vision"
        }
    },
    "97:50": {
        "class_type": "WanImageToVideo",
        "inputs": {
            "batch_size": 1,
            "clip_vision_output": [
                "97:51",
                0
            ],
            "height": 720,
            "length": 5,
            "negative": [
                "97:7",
                0
            ],
            "positive": [
                "97:6",
                0
            ],
            "start_image": [
                "52",
                0
            ],
            "vae": [
                "97:39",
                0
            ],
            "width": 720
        },
        "_meta": {
            "title": "WanImageToVideo"
        }
    },
    "97:51": {
        "class_type": "CLIPVisionEncode",
        "inputs": {
            "clip_vision": [
                "97:49",
                0
            ],
            "crop": "none",
            "image": [
                "52",
                0
            ]
        },
        "_meta": {
            "title": "CLIP Vision Encode"
        }
    },
    "97:54": {
        "class_type": "ModelSamplingSD3",
        "inputs": {
            "model": [
                "97:37",
                0
            ],
            "shift": 5
        },
        "_meta": {
            "title": "ModelSamplingSD3"
        }
    },
    "97:58": {
        "class_type": "ScaleROPE",
        "inputs": {
            "model": [
                "97:54",
                0
            ],
            "scale_t": 7,
            "scale_x": 1,
            "scale_y": 1,
            "shift_t": 0,
            "shift_x": 0,
            "shift_y": 0
        },
        "_meta": {
            "title": "ScaleROPE"
        }
    },
    "97:59": {
        "class_type": "ImageFromBatch",
        "inputs": {
            "batch_index": 4,
            "image": [
                "97:8",
                0
            ],
            "length": 1
        },
        "_meta": {
            "title": "Get Image from Batch"
        }
    },
    "97:6": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "97:38",
                0
            ],
            "text": "A bottle of facial cleansing foam and bubble shampoo, surrounded by white, round, foamy bubbles. The bubbles are very fluffy, crystal clear, giving a sense of fluffiness and comfort. There are also several bubbles floating in the air around, and the bottle is floating in the air. The background is light pink. The high-resolution picture creates a professional advertising style with high-definition images and high-quality details."
        },
        "_meta": {
            "title": "CLIP Text Encode (Positive Prompt)"
        }
    },
    "97:7": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "97:38",
                0
            ],
            "text": "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
        },
        "_meta": {
            "title": "CLIP Text Encode (Negative Prompt)"
        }
    },
    "97:8": {
        "class_type": "VAEDecode",
        "inputs": {
            "samples": [
                "97:3",
                0
            ],
            "vae": [
                "97:39",
                0
            ]
        },
        "_meta": {
            "title": "VAE Decode"
        }
    }
})

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