Qwen-Image-Layered Control Decomposition
Describe the elements you want in the image and decompose it precisely from the image.
概要
The Qwen-Image-Layered Control Decomposition workflow is designed to help users decompose an image into its constituent elements using the Qwen-Image-Layered model. This workflow is particularly useful for artists and designers who need to isolate specific parts of an image for further manipulation or analysis.
The process begins with loading an image using the LoadImage node, followed by scaling it to the desired dimensions with the ImageScaleToMaxDimension node. The core of the workflow involves the f754a936-daaf-4b6e-9658-41fdc54d301d node, which utilizes the Qwen-Image-Layered model to perform the decomposition.
This workflow is technically robust, allowing for precise control over image layers, which can be saved using the SaveImage node. The MarkdownNote node is used to document the process and provide guidance throughout the workflow. This layered decomposition is beneficial in scenarios where detailed image editing is required, such as in graphic design, digital art, and even in scientific imaging where specific features need to be isolated and analyzed.
入力するもの
- ワークフローを読み込み中
ComfyUI で実行できます。 このワークフローはこのページでは実行できません。すべてが読み込まれた状態でComfy Cloudで開くか、ご自身のComfyUI用にダウンロードしてください。
Cloudで試すワークフローの内部
ノードグラフの読み取り専用プレビューです。ノードを編集するには、Comfy Cloudで開くかダウンロードしてください。
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Open full-size workflow preview- 作成者
- ComfyUI
カスタムノードが必要です
- ワークフローを読み込み中
コードからこのワークフローを使用する
可能な場合はワークフローグラフと入力から生成されます。それ以外の場合は保存された例が表示されます。料金が発生する場合があるため、コードを実行する前に認証情報とエンドポイントを確認してください。
What you need
- Runs on
- Comfy Cloud
- キー
- COMFY_API_KEY
有料の Cloud プランとクレジットが必要です。
# 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({
"60": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "Qwen-image-layers",
"images": [
"83:8",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"74": {
"class_type": "LoadImage",
"inputs": {
"image": "raccoon_rocker.png"
},
"_meta": {
"title": "Load Image"
}
},
"82": {
"class_type": "ImageScaleToMaxDimension",
"inputs": {
"image": [
"74",
0
],
"largest_size": 640,
"upscale_method": "lanczos"
},
"_meta": {
"title": "Scale Image to Max Dimension"
}
},
"83:3": {
"class_type": "KSampler",
"inputs": {
"cfg": 2.5,
"denoise": 1,
"latent_image": [
"83:83",
0
],
"model": [
"83:66",
0
],
"negative": [
"83:70",
0
],
"positive": [
"83:69",
0
],
"sampler_name": "euler",
"scheduler": "simple",
"seed": 657641993490688,
"steps": 20
},
"_meta": {
"title": "KSampler"
}
},
"83:37": {
"class_type": "UNETLoader",
"inputs": {
"unet_name": "qwen_image_layered_control_bf16.safetensors",
"weight_dtype": "fp8_e4m3fn"
},
"_meta": {
"title": "Load Diffusion Model"
}
},
"83:38": {
"class_type": "CLIPLoader",
"inputs": {
"clip_name": "qwen_2.5_vl_7b_fp8_scaled.safetensors",
"device": "default",
"type": "qwen_image"
},
"_meta": {
"title": "Load CLIP"
}
},
"83:39": {
"class_type": "VAELoader",
"inputs": {
"vae_name": "qwen_image_layered_vae.safetensors"
},
"_meta": {
"title": "Load VAE"
}
},
"83:6": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"83:38",
0
],
"text": "guitar"
},
"_meta": {
"title": "CLIP Text Encode (Positive Prompt)"
}
},
"83:66": {
"class_type": "ModelSamplingAuraFlow",
"inputs": {
"model": [
"83:37",
0
],
"sampling": "flow",
"shift": 1
},
"_meta": {
"title": "ModelSamplingAuraFlow"
}
},
"83:69": {
"class_type": "ReferenceLatent",
"inputs": {
"conditioning": [
"83:6",
0
],
"latent": [
"83:71",
0
]
},
"_meta": {
"title": "Set Reference Latent"
}
},
"83:7": {
"class_type": "CLIPTextEncode",
"inputs": {
"clip": [
"83:38",
0
],
"text": ""
},
"_meta": {
"title": "CLIP Text Encode (Negative Prompt)"
}
},
"83:70": {
"class_type": "ReferenceLatent",
"inputs": {
"conditioning": [
"83:7",
0
],
"latent": [
"83:71",
0
]
},
"_meta": {
"title": "Set Reference Latent"
}
},
"83:71": {
"class_type": "VAEEncode",
"inputs": {
"pixels": [
"82",
0
],
"vae": [
"83:39",
0
]
},
"_meta": {
"title": "VAE Encode"
}
},
"83:76": {
"class_type": "LatentCutToBatch",
"inputs": {
"dim": "t",
"samples": [
"83:3",
0
],
"slice_size": 1
},
"_meta": {
"title": "LatentCutToBatch"
}
},
"83:78": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"82",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"83:8": {
"class_type": "VAEDecode",
"inputs": {
"samples": [
"83:76",
0
],
"vae": [
"83:39",
0
]
},
"_meta": {
"title": "VAE Decode"
}
},
"83:83": {
"class_type": "EmptyQwenImageLayeredLatentImage",
"inputs": {
"batch_size": 1,
"height": [
"83:78",
1
],
"layers": 2,
"width": [
"83:78",
0
]
},
"_meta": {
"title": "Empty Qwen Image Layered Latent"
}
}
})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)よくある質問
The recommended input size is 640px for standard use, and 1024px for high-resolution needs.
The model is computationally intensive, with original sampling settings of 50 steps and a CFG of 4.0, which can double the generation time.
This workflow is optimized for still images. For video, consider using a tool specifically designed for video processing.
You can learn more about subgraphs by visiting the Subgraph docs provided in the workflow.