Z-Image-Turbo Fun Union ControlNet

Multi-control ControlNet supporting Canny, HED, Depth, Pose, and MLSD for Z-Image-Turbo.

下载工作流在 Cloud 中试用

Input

Control image

Prompt

Advanced settings

Canny high threshold

Default: 0.32

Canny low threshold

Default: 0.1

Seed

Steps

Default: 9

Control strength

Default: 1

Run

Output

Example

从代码中调用该模型

以下请求使用与体验区相同的预设图和设置。每次提交都可能开始一次付费运行；如果提交结果未知，请勿自动重试。

获取 API 密钥

What you need

Runs on: Comfy Cloud
密钥: COMFY_API_KEY
你的文件: 由代码根据链接上传

需要付费的 Cloud 方案和额度。

下载 API JSON

# 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": "z-image-turbo",
            "images": [
                "70:43",
                0
            ]
        },
        "_meta": {
            "title": "Save Image"
        }
    },
    "56": {
        "class_type": "PreviewImage",
        "inputs": {
            "images": [
                "57",
                0
            ]
        },
        "_meta": {
            "title": "Preview Image"
        }
    },
    "57": {
        "class_type": "Canny",
        "inputs": {
            "high_threshold": 0.32,
            "image": [
                "58",
                0
            ],
            "low_threshold": 0.1
        },
        "_meta": {
            "title": "Detect Edges (Canny)"
        }
    },
    "58": {
        "class_type": "LoadImage",
        "inputs": {
            "image": "UPLOADED_image_FILENAME"
        },
        "_meta": {
            "title": "Load Image"
        }
    },
    "70:39": {
        "class_type": "CLIPLoader",
        "inputs": {
            "clip_name": "qwen_3_4b.safetensors",
            "device": "default",
            "type": "lumina2"
        },
        "_meta": {
            "title": "Load CLIP"
        }
    },
    "70:40": {
        "class_type": "VAELoader",
        "inputs": {
            "vae_name": "ae.safetensors"
        },
        "_meta": {
            "title": "Load VAE"
        }
    },
    "70:41": {
        "class_type": "EmptySD3LatentImage",
        "inputs": {
            "batch_size": 1,
            "height": [
                "70:69",
                1
            ],
            "width": [
                "70:69",
                0
            ]
        },
        "_meta": {
            "title": "EmptySD3LatentImage"
        }
    },
    "70:42": {
        "class_type": "ConditioningZeroOut",
        "inputs": {
            "conditioning": [
                "70:45",
                0
            ]
        },
        "_meta": {
            "title": "Conditioning Zero Out"
        }
    },
    "70:43": {
        "class_type": "VAEDecode",
        "inputs": {
            "samples": [
                "70:44",
                0
            ],
            "vae": [
                "70:40",
                0
            ]
        },
        "_meta": {
            "title": "VAE Decode"
        }
    },
    "70:44": {
        "class_type": "KSampler",
        "inputs": {
            "cfg": 1,
            "denoise": 1,
            "latent_image": [
                "70:41",
                0
            ],
            "model": [
                "70:47",
                0
            ],
            "negative": [
                "70:42",
                0
            ],
            "positive": [
                "70:45",
                0
            ],
            "sampler_name": "res_multistep",
            "scheduler": "simple",
            "seed": 582911328872997,
            "steps": 9
        },
        "_meta": {
            "title": "KSampler"
        }
    },
    "70:45": {
        "class_type": "CLIPTextEncode",
        "inputs": {
            "clip": [
                "70:39",
                0
            ],
            "text": "Realistic photo of a moonlit greenhouse library at night, built inside the same room layout. The ceiling slats become dark walnut beams strung with tiny warm bulbs, and the tall window wall turns into old greenhouse glass fogged with condensation, tropical leaves pressing against it from outside. The low sofa is upholstered in deep emerald velvet, the stone coffee table becomes a slab of black marble stacked with open books, and the lounge chair is worn burgundy leather. The shelving grid on the right holds trailing ferns and small brass lanterns. Cool blue moonlight falls through the glass, warm lamplight pools on the terrazzo floor, quiet and cosy, light film grain."
        },
        "_meta": {
            "title": "CLIP Text Encode (Prompt)"
        }
    },
    "70:46": {
        "class_type": "UNETLoader",
        "inputs": {
            "unet_name": "z_image_turbo_bf16.safetensors",
            "weight_dtype": "default"
        },
        "_meta": {
            "title": "Load Diffusion Model"
        }
    },
    "70:47": {
        "class_type": "ModelSamplingAuraFlow",
        "inputs": {
            "model": [
                "70:60",
                0
            ],
            "sampling": "flow",
            "shift": 3
        },
        "_meta": {
            "title": "ModelSamplingAuraFlow"
        }
    },
    "70:60": {
        "class_type": "QwenImageDiffsynthControlnet",
        "inputs": {
            "end_percent": 1,
            "image": [
                "57",
                0
            ],
            "model": [
                "70:46",
                0
            ],
            "model_patch": [
                "70:64",
                0
            ],
            "start_percent": 0,
            "strength": 1,
            "vae": [
                "70:40",
                0
            ]
        },
        "_meta": {
            "title": "Apply Qwen Image DiffSynth ControlNet"
        }
    },
    "70:64": {
        "class_type": "ModelPatchLoader",
        "inputs": {
            "name": "Z-Image-Turbo-Fun-Controlnet-Union.safetensors"
        },
        "_meta": {
            "title": "Load Model Patch"
        }
    },
    "70:69": {
        "class_type": "GetImageSize",
        "inputs": {
            "image": [
                "57",
                0
            ]
        },
        "_meta": {
            "title": "Get Image Size"
        }
    }
})
workflow.set_input("58", "image", client.assets.from_url("https://comfy.org/catalog-media/d582dae6-4b4a-4ef9-9a66-ce170401a12d/6ac33887-7efb-472b-b4bf-e29fa62ab9d3"))

# 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)

API 文档

试试一个示例

关于此工作流

The 'Z-Image-Turbo Fun Union ControlNet' workflow is a sophisticated image generation process that leverages multiple ControlNet models to enhance and manipulate images with precision. This workflow integrates various image preprocessing techniques, including Canny edge detection, HED (Holistically-Nested Edge Detection), Depth estimation, Pose recognition, and MLSD (Multi-Line Segment Detection). These techniques are orchestrated to refine and control the output of the Z-Image-Turbo model, allowing for nuanced and detailed image transformations.

By utilizing nodes such as SaveImage, MarkdownNote, PreviewImage, and ImageScaleToMaxDimension, the workflow ensures that images are processed efficiently and effectively.

Technically, this workflow stands out due to its ability to handle multiple control inputs simultaneously, providing users with a high degree of control over the image generation process. The integration of various ControlNet models allows users to apply different preprocessing techniques to the input images, which are then scaled and transformed according to the desired output specifications. This makes the workflow particularly useful for applications that require precise image manipulation, such as digital art creation, augmented reality, and advanced photo editing.

Z-Image-Turbo Fun Union ControlNet

Input

查看工作流内部

从代码中调用该模型

What you need

试试一个示例

关于此工作流

常见问答

Input

查看工作流内部

从代码中调用该模型

What you need

试试一个示例

关于此工作流

常见问答

What is the benefit of using multiple ControlNet models?

Can I use this workflow for real-time applications?

How can I adjust the image dimensions?

Is this workflow suitable for beginners?