Profile Picture Stylized Variations
Upload your profile picture, enter a theme and generate 64 variations.
ワークフローをダウンロードCloudで試す API ドキュメント
概要
The 'Profile Picture Stylized Variations' workflow is designed to creatively transform your profile picture into a set of 64 unique variations based on a specified theme. This workflow leverages a combination of advanced AI models and custom nodes to achieve high-quality, diverse outputs. It begins with the 'LoadImage' node, which allows you to upload your profile picture. The image is then processed through the 'Nano Banana Pro' model, known for its ability to apply stylistic transformations effectively.
The 'Gemini3 Pro Image Preview' model is used to ensure that each variation maintains a high level of detail and artistic quality. Custom nodes such as 'comfyui-kjnodes' and 'comfyui_essentials' are integrated to enhance the workflow's flexibility and output diversity. This workflow is particularly useful for social media enthusiasts and professionals looking to refresh their online presence with creative and thematic profile picture variations.
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で試す例
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。
Loading the graph
Open full-size workflow preview- 作成者
- Rob
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。
1 API キーを取得 ↗
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。
API JSONをダウンロードAPI ドキュメント ↗
# 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({
"10": {
"class_type": "LoadImage",
"inputs": {
"image": "subject-profile.png"
},
"_meta": {
"title": "Load Image"
}
},
"16": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 1
},
"_meta": {
"title": "Select Image Number"
}
},
"19": {
"class_type": "GeminiImage2Node",
"inputs": {
"aspect_ratio": "auto",
"images": [
"17:78",
0
],
"model": "gemini-3-pro-image-preview",
"prompt": "Analyze the input image to understand it's aesthetic qualities. Upscale image 1 and add subtle details, fix text while maintaining the subject's identity.",
"resolution": "2K",
"response_modalities": "IMAGE+TEXT",
"seed": 222,
"system_prompt": "You are an expert image-generation engine. You must ALWAYS produce an image.\nInterpret all user input—regardless of format, intent, or abstraction—as literal visual directives for image composition.\nIf a prompt is conversational or lacks specific visual details, you must creatively invent a concrete visual scenario that depicts the concept.\nPrioritize generating the visual representation above any text, formatting, or conversational requests."
},
"_meta": {
"title": "Nano Banana Pro (Google Gemini Image)"
}
},
"32": {
"class_type": "GeminiImage2Node",
"inputs": {
"aspect_ratio": "1:1",
"images": [
"10",
0
],
"model": "gemini-3-pro-image-preview",
"prompt": "Generate a 8x8 grid (8 rows, 8 cols) of variations of this profile picture used for social media. There should be 64 total images. Each grid image should have a unique visual appearance.\n\nThe theme for the grid images is: glitch art",
"resolution": "4K",
"response_modalities": "IMAGE+TEXT",
"seed": 111,
"system_prompt": "You are an expert image-generation engine. You must ALWAYS produce an image.\nInterpret all user input—regardless of format, intent, or abstraction—as literal visual directives for image composition.\nIf a prompt is conversational or lacks specific visual details, you must creatively invent a concrete visual scenario that depicts the concept.\nPrioritize generating the visual representation above any text, formatting, or conversational requests."
},
"_meta": {
"title": "Nano Banana Pro (Google Gemini Image)"
}
},
"36": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-selected_image-upscaled",
"images": [
"19",
0
]
},
"_meta": {
"title": "Save Upscaled Image"
}
},
"37": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-selected_image",
"images": [
"17:78",
0
]
},
"_meta": {
"title": "Save Selected Image"
}
},
"39": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-64_grid_images",
"images": [
"32",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"15:101": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"15:66",
0
],
"image": [
"32",
0
],
"width": [
"15:65",
0
],
"x": 0,
"y": [
"15:63",
0
]
},
"_meta": {
"title": "9"
}
},
"15:102": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"15:66",
0
],
"image": [
"32",
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],
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"15:65",
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],
"x": [
"15:59",
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],
"y": [
"15:63",
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]
},
"_meta": {
"title": "10"
}
},
"15:103": {
"class_type": "ImageCrop",
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"height": [
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],
"image": [
"32",
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},
"_meta": {
"title": "11"
}
},
"15:104": {
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],
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"32",
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},
"_meta": {
"title": "12"
}
},
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"_meta": {
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}
},
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"_meta": {
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}
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"y": [
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},
"_meta": {
"title": "15"
}
},
"15:144": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"15:66",
0
],
"b": 0,
"value": "2*a"
},
"_meta": {
"title": "Row 3 - Y val"
}
},
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"_meta": {
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},
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"_meta": {
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},
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},
"15:150": {
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"image": [
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"_meta": {
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},
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"_meta": {
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"_meta": {
"title": "24"
}
},
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"inputs": {
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"_meta": {
"title": "Image Batch Multi"
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},
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},
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],
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],
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"title": "46"
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},
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},
"_meta": {
"title": "48"
}
},
"15:287": {
"class_type": "ImageBatchMulti",
"inputs": {
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],
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"image_8": [
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],
"inputcount": 8
},
"_meta": {
"title": "Image Batch Multi"
}
},
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"class_type": "ImageCrop",
"inputs": {
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],
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],
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},
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"title": "49"
}
},
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],
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"_meta": {
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},
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}
},
"15:316": {
"class_type": "ImageCrop",
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},
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"image_8": [
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"inputcount": 8
},
"_meta": {
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}
},
"15:321": {
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"_meta": {
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},
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"image_2": [
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],
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],
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"x": [
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},
"_meta": {
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}
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"inputs": {
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"b": 0,
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},
"_meta": {
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}
},
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"b": 0,
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}
},
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"b": 0,
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"height": [
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}
},
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"inputs": {
"a": [
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"b": 0,
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}
},
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"class_type": "SimpleMath+",
"inputs": {
"a": [
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0
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"b": 0,
"value": "4*a"
},
"_meta": {
"title": "Col 5 - X val"
}
},
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"inputs": {
"a": [
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"b": 0,
"value": "5*a"
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}
},
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"class_type": "SimpleMath+",
"inputs": {
"a": [
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0
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"b": 0,
"value": "6*a"
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},
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"height": [
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0
],
"image": [
"32",
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"x": [
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"y": 0
},
"_meta": {
"title": "6"
}
},
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"class_type": "ImageCrop",
"inputs": {
"height": [
"15:66",
0
],
"image": [
"32",
0
],
"width": [
"15:65",
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],
"x": [
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"y": 0
},
"_meta": {
"title": "7"
}
},
"15:94": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"15:65",
0
],
"b": 0,
"value": "7*a"
},
"_meta": {
"title": "Col 8 - X val"
}
},
"15:95": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"15:66",
0
],
"image": [
"32",
0
],
"width": [
"15:65",
0
],
"x": [
"15:94",
0
],
"y": 0
},
"_meta": {
"title": "8"
}
},
"17:78": {
"class_type": "ImageFromBatch",
"inputs": {
"batch_index": [
"17:89",
0
],
"image": [
"15:333",
0
],
"length": 1
},
"_meta": {
"title": "Get Image from Batch"
}
},
"17:83": {
"class_type": "ResizeAndPadImage",
"inputs": {
"image": [
"10",
0
],
"interpolation": "area",
"padding_color": "white",
"target_height": [
"17:86",
1
],
"target_width": [
"17:86",
0
]
},
"_meta": {
"title": "Resize And Pad Image"
}
},
"17:86": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"17:78",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"17:87": {
"class_type": "ImageBatch",
"inputs": {
"image1": [
"17:78",
0
],
"image2": [
"17:83",
0
]
},
"_meta": {
"title": "Batch Images (DEPRECATED)"
}
},
"17:89": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"16",
0
],
"b": 0,
"value": "a - 1"
},
"_meta": {
"title": "Image # as index"
}
}
})
# 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
Yes, you can upload any image, but for best results, use a clear and high-resolution profile picture.
You can use any theme that inspires you, such as 'futuristic', 'vintage', or 'abstract'. The models are designed to interpret and apply a wide range of themes.
The processing time depends on your system's capabilities, but typically it takes a few minutes to generate all 64 variations.
The workflow is set to generate 64 variations by default, but you can adjust this by modifying the parameters in the 'PrimitiveInt' node.