Character × 4 Scene Explorer
Upload a reference image and generate a 1x4 grid. Select desired image and upscale/refine with Nano Banana Pro.
About
The 'Character × 4 Scene Explorer' workflow is designed to help users generate a variety of character images based on a single reference image. This workflow leverages the power of Google Gemini Image and Nano Banana Pro models to create a 1x4 grid of character variations. By using nodes such as LoadImage and GeminiImage2Node, the workflow first processes the input image to produce four distinct character scenes. Once generated, users can select their preferred image for further refinement and upscaling using the Nano Banana Pro model, ensuring high-quality output.
Technically, this workflow utilizes several custom nodes, including those from comfyui-kjnodes and comfyui_essentials, which streamline the image processing and refinement tasks. Nodes like Reroute, PrimitiveInt, and RegexReplace are employed to manage data flow and transform inputs efficiently. This workflow is particularly useful for artists and designers looking to explore multiple character design options quickly, as it automates the generation and refinement process, saving time and enhancing creativity.
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 CloudInside 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.
What you need
- Runs on
- Comfy Cloud
- Key
- COMFY_API_KEY
Needs a paid Cloud plan and credits.
# 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({
"1": {
"class_type": "LoadImage",
"inputs": {
"image": "cube_robot.jpg"
},
"_meta": {
"title": "Load Image"
}
},
"2": {
"class_type": "GeminiImage2Node",
"inputs": {
"aspect_ratio": "9:16",
"images": [
"1",
0
],
"model": "gemini-3-pro-image-preview",
"prompt": [
"24",
0
],
"resolution": "4K",
"response_modalities": "IMAGE+TEXT",
"seed": 123,
"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)"
}
},
"3": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-split_stack",
"images": [
"2",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"10": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_1",
"images": [
"7:29",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"11": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_2",
"images": [
"7:101",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"12": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_2",
"images": [
"7:187",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"13": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_1",
"images": [
"7:145",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"15": {
"class_type": "GeminiImage2Node",
"inputs": {
"aspect_ratio": "16:9",
"images": [
"14:78",
0
],
"model": "gemini-3-pro-image-preview",
"prompt": "Upscale image 1 and add subtle details. Crop if necessary.",
"resolution": "4K",
"response_modalities": "IMAGE+TEXT",
"seed": 321,
"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)"
}
},
"16": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-selected_image-upscaled",
"images": [
"15",
0
]
},
"_meta": {
"title": "Save Upscaled Image"
}
},
"18": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-selected_image",
"images": [
"14:78",
0
]
},
"_meta": {
"title": "Save Selected Image"
}
},
"21": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 1
},
"_meta": {
"title": "Select Image Number"
}
},
"22": {
"class_type": "PrimitiveStringMultiline",
"inputs": {
"value": "The subject is inside a giant computer motherboard landscape - towering capacitors, circuit trace pathways like roads, and LED lights creating a Tron-like microverse where the robot fits the scale perfectly. Dark eerie atmosphere."
},
"_meta": {
"title": "Describe the Scene"
}
},
"23": {
"class_type": "PrimitiveStringMultiline",
"inputs": {
"value": "Use the input image. Divide the frame in 4 different stacked 16:9 frames.\nFor each frame choose the angle and type of shot (close up, extreme close up, dutch, over-shoulder, wide shot, Cowboy shot, full shot, etc).\n\nEnsure that subject is present in each frame and the environment is consistent between frames:\n{scene}"
},
"_meta": {
"title": "Prompt Template"
}
},
"24": {
"class_type": "RegexReplace",
"inputs": {
"case_insensitive": True,
"count": 0,
"dotall": False,
"multiline": False,
"regex_pattern": "{scene}",
"replace": [
"22",
0
],
"string": [
"23",
0
]
},
"_meta": {
"title": "Replace Text (Regex)"
}
},
"14:78": {
"class_type": "ImageFromBatch",
"inputs": {
"batch_index": [
"14:89",
0
],
"image": [
"7:333",
0
],
"length": 1
},
"_meta": {
"title": "Get Image from Batch"
}
},
"14:83": {
"class_type": "ResizeAndPadImage",
"inputs": {
"image": [
"1",
0
],
"interpolation": "area",
"padding_color": "white",
"target_height": [
"14:86",
1
],
"target_width": [
"14:86",
0
]
},
"_meta": {
"title": "Resize And Pad Image"
}
},
"14:86": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"14:78",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"14:87": {
"class_type": "ImageBatch",
"inputs": {
"image1": [
"14:78",
0
],
"image2": [
"14:83",
0
]
},
"_meta": {
"title": "Batch Images (DEPRECATED)"
}
},
"14:89": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"21",
0
],
"b": 0,
"value": "a - 1"
},
"_meta": {
"title": "Image # as index"
}
},
"7:101": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"7:66",
0
],
"image": [
"2",
0
],
"width": [
"7:65",
0
],
"x": 0,
"y": [
"7:63",
0
]
},
"_meta": {
"title": "2"
}
},
"7:109": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"7:101",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"7:136": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"7:145",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"7:144": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"7:66",
0
],
"b": 0,
"value": "2*a"
},
"_meta": {
"title": "Row 3 - Y val"
}
},
"7:145": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"7:66",
0
],
"image": [
"2",
0
],
"width": [
"7:65",
0
],
"x": 0,
"y": [
"7:144",
0
]
},
"_meta": {
"title": "3"
}
},
"7:164": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"7:187",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"7:172": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"7:66",
0
],
"b": 0,
"value": "3*a"
},
"_meta": {
"title": "Row 4 - Y val"
}
},
"7:18": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"2",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"7:187": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"7:66",
0
],
"image": [
"2",
0
],
"width": [
"7:65",
0
],
"x": 0,
"y": [
"7:172",
0
]
},
"_meta": {
"title": "4"
}
},
"7:29": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"7:66",
0
],
"image": [
"2",
0
],
"width": [
"7:65",
0
],
"x": 0,
"y": 0
},
"_meta": {
"title": "1"
}
},
"7:32": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"7:29",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"7:333": {
"class_type": "ImageBatchMulti",
"inputs": {
"image_1": [
"7:29",
0
],
"image_2": [
"7:101",
0
],
"image_3": [
"7:145",
0
],
"image_4": [
"7:187",
0
],
"inputcount": 4
},
"_meta": {
"title": "Image Batch Multi"
}
},
"7:353": {
"class_type": "SimpleMath+",
"inputs": {
"a": 0,
"b": 0,
"value": "(2*a) + (3*b)"
},
"_meta": {
"title": "Row 3 - Y val"
}
},
"7:63": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"7:66",
0
],
"b": 0,
"value": "a"
},
"_meta": {
"title": "Row 2 - Y val"
}
},
"7:65": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"7:18",
0
],
"b": 0,
"value": "a"
},
"_meta": {
"title": "WIDTH"
}
},
"7:66": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"7:18",
1
],
"b": 0,
"value": "a / 4"
},
"_meta": {
"title": "HEIGHT"
}
}
})
# 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 as a reference, but results may vary based on the quality and content of the image.
You can rerun the workflow with different parameters or a new reference image to generate new variations.
The Nano Banana Pro model refines and upscales images, improving details and resolution for a polished final output.
Yes, the workflow is designed to be user-friendly, with step-by-step instructions to guide users through the process.