Profile Picture Stylized Variations
Upload your profile picture, enter a theme and generate 64 variations.
Download WorkflowTry in Cloud API documentation
About
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.
What you provide
- Reading the workflow
Runs in ComfyUI. This workflow can't run on this page. Open it in Comfy Cloud with everything loaded, or download it for your own ComfyUI.
Try in CloudExample
Inside the workflow
A read-only preview of the node graph. Open it in Comfy Cloud or download it to edit the nodes.
Loading the graph
Open full-size workflow preview- Author
- Rob
Requires custom nodes
- Reading the workflow
Use this workflow from code
Generated from the workflow graph and inputs when possible; stored examples are shown otherwise. Review credentials and endpoints before running code, which may incur charges.
1 Get API key ↗
What you need
- Runs on
- Comfy Cloud
- Key
- COMFY_API_KEY
Needs a paid Cloud plan and credits.
Download API JSONAPI documentation ↗
# 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",
0
],
"width": [
"15:65",
0
],
"x": [
"15:59",
0
],
"y": [
"15:63",
0
]
},
"_meta": {
"title": "10"
}
},
"15:103": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"15:66",
0
],
"image": [
"32",
0
],
"width": [
"15:65",
0
],
"x": [
"15:60",
0
],
"y": [
"15:63",
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]
},
"_meta": {
"title": "11"
}
},
"15:104": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"15:66",
0
],
"image": [
"32",
0
],
"width": [
"15:65",
0
],
"x": [
"15:82",
0
],
"y": [
"15:63",
0
]
},
"_meta": {
"title": "12"
}
},
"15:105": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"15:66",
0
],
"image": [
"32",
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],
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],
"x": [
"15:83",
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],
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]
},
"_meta": {
"title": "13"
}
},
"15:106": {
"class_type": "ImageCrop",
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],
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"32",
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},
"_meta": {
"title": "14"
}
},
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"class_type": "ImageCrop",
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"height": [
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],
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"32",
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"_meta": {
"title": "16"
}
},
"15:114": {
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],
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"32",
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],
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],
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"15:87",
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],
"y": [
"15:63",
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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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"class_type": "ImageCrop",
"inputs": {
"height": [
"15:66",
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],
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"32",
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"_meta": {
"title": "17"
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"_meta": {
"title": "19"
}
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"_meta": {
"title": "20"
}
},
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"class_type": "ImageCrop",
"inputs": {
"height": [
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"image": [
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"_meta": {
"title": "21"
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"class_type": "ImageCrop",
"inputs": {
"height": [
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"image": [
"32",
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"_meta": {
"title": "22"
}
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"_meta": {
"title": "23"
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"_meta": {
"title": "24"
}
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"image_6": [
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"image_7": [
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"image_8": [
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"inputcount": 8
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"_meta": {
"title": "Image Batch Multi"
}
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"class_type": "ImageBatchMulti",
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"image_8": [
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"inputcount": 8
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"_meta": {
"title": "Image Batch Multi"
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"image_8": [
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"inputcount": 8
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"_meta": {
"title": "Image Batch Multi"
}
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"15:172": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"15:66",
0
],
"b": 0,
"value": "3*a"
},
"_meta": {
"title": "Row 4 - Y val"
}
},
"15:18": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"32",
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]
},
"_meta": {
"title": "Get Image Size"
}
},
"15:186": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"15:66",
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],
"b": 0,
"value": "4*a"
},
"_meta": {
"title": "Row 5 - Y val"
}
},
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"class_type": "ImageCrop",
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"y": [
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"_meta": {
"title": "32"
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"15:195": {
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"inputs": {
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"image_2": [
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"image_3": [
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"inputcount": 8
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"_meta": {
"title": "Image Batch Multi"
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"title": "Image Batch Multi"
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"_meta": {
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},
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"interpolation": "area",
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"_meta": {
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},
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"inputs": {
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]
},
"_meta": {
"title": "Get Image Size"
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},
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"inputs": {
"image1": [
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],
"image2": [
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},
"_meta": {
"title": "Batch Images (DEPRECATED)"
}
},
"17:89": {
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"inputs": {
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})
# run() also passes api_key to the partner nodes in this workflow.
job = client.run(workflow, api_key=api_key)Questions & answers
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.