Poster Scene Mockups
Upload a poster/ad design and with a short input about your brand, generate 4 mockups in multiple scenes.
概要
The 'Poster Scene Mockups' workflow is designed to streamline the process of creating realistic mockups for poster or advertisement designs. By leveraging the power of AI models such as Nano Banana Pro and Google, this workflow allows users to input a design and a brief description of their brand to generate four distinct mockups in various scenes.
The workflow utilizes a series of nodes, including the GeminiImage2Node for image processing, LoadImage for uploading the initial design, and SaveImage for storing the final mockups. The comfyui_essentials custom nodes are integral to this workflow, providing additional functionalities that enhance the image editing and mockup generation process.
Technically, the workflow begins with the LoadImage node, which imports the user's design. The PrimitiveStringMultiline node captures the brand description, which is then processed alongside the design through nodes like RegexReplace and ResizeAndPadImage to ensure the mockups are correctly formatted and visually appealing. The ImageBatch and ImageFromBatch nodes are used to handle multiple images efficiently, while SimpleMath+ and GetImageSize help in adjusting dimensions and scaling.
This workflow is particularly useful for designers and marketers looking to quickly visualize how their posters will appear in real-world settings, saving time and resources in the mockup creation process.
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
- 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。
What you need
- Runs on
- Comfy Cloud
- キー
- COMFY_API_KEY
- ファイル
- コードによって URL からアップロードされます
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({
"1": {
"class_type": "GeminiImage2Node",
"inputs": {
"aspect_ratio": "2:3",
"images": [
"2",
0
],
"model": "gemini-3-pro-image-preview",
"prompt": [
"41",
0
],
"resolution": "4K",
"response_modalities": "IMAGE",
"seed": 333,
"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)"
}
},
"2": {
"class_type": "LoadImage",
"inputs": {
"image": "UPLOADED_image_FILENAME"
},
"_meta": {
"title": "Load Poster Image"
}
},
"3": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_stitched",
"images": [
"1",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"18": {
"class_type": "PrimitiveInt",
"inputs": {
"value": 0
},
"_meta": {
"title": "Crop Inset Grid Images"
}
},
"20": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_1",
"images": [
"19:8",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"21": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_2",
"images": [
"19:9",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"22": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_3",
"images": [
"19:10",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"23": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_4",
"images": [
"19:11",
0
]
},
"_meta": {
"title": "Save Image"
}
},
"24": {
"class_type": "PrimitiveStringMultiline",
"inputs": {
"value": "Create a 2x2 grid composition featuring four distinct, high-quality mockups. Populate each of the four panels by integrating the poster shown in image 1 (maintain the input posters aspect ratio) into various {type}.\n\nThe entire grid must strictly adhere to a {aesthetic} aesthetic.\n\nApply coordinated {lighting} across all four scenarios to ensure visual consistency. Ensure the scenes in each panel are varied use-cases and look authentic to the chosen medium."
},
"_meta": {
"title": "Prompt Template"
}
},
"25": {
"class_type": "RegexReplace",
"inputs": {
"case_insensitive": True,
"count": 0,
"dotall": False,
"multiline": False,
"regex_pattern": "{type}",
"replace": [
"26",
0
],
"string": [
"24",
0
]
},
"_meta": {
"title": "Replace Text (Regex)"
}
},
"26": {
"class_type": "PrimitiveStringMultiline",
"inputs": {
"value": "billboards"
},
"_meta": {
"title": "Describe the Mockup Medium"
}
},
"27": {
"class_type": "PrimitiveStringMultiline",
"inputs": {
"value": "minimalist in a clean neutral toned environment"
},
"_meta": {
"title": "Describe the Aesthetic"
}
},
"28": {
"class_type": "RegexReplace",
"inputs": {
"case_insensitive": True,
"count": 0,
"dotall": False,
"multiline": False,
"regex_pattern": "{aesthetic}",
"replace": [
"27",
0
],
"string": [
"25",
0
]
},
"_meta": {
"title": "Replace Text (Regex)"
}
},
"30": {
"class_type": "ResizeAndPadImage",
"inputs": {
"image": [
"2",
0
],
"interpolation": "area",
"padding_color": "white",
"target_height": [
"34",
1
],
"target_width": [
"34",
0
]
},
"_meta": {
"title": "Resize And Pad Image"
}
},
"31": {
"class_type": "ImageBatch",
"inputs": {
"image1": [
"32",
0
],
"image2": [
"30",
0
]
},
"_meta": {
"title": "Batch Images (DEPRECATED)"
}
},
"32": {
"class_type": "ImageFromBatch",
"inputs": {
"batch_index": [
"33",
0
],
"image": [
"19:19",
0
],
"length": 1
},
"_meta": {
"title": "Get Image from Batch"
}
},
"33": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"36",
0
],
"b": 0,
"value": "a - 1"
},
"_meta": {
"title": "Image # as index"
}
},
"34": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"32",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"35": {
"class_type": "GeminiImage2Node",
"inputs": {
"aspect_ratio": "auto",
"images": [
"31",
0
],
"model": "gemini-3-pro-image-preview",
"prompt": "Upscale image 1. Use the poster from image 2 as a reference to fix the text.",
"resolution": "4K",
"response_modalities": "IMAGE+TEXT",
"seed": 54321,
"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": "PrimitiveInt",
"inputs": {
"value": 1
},
"_meta": {
"title": "Select Image #"
}
},
"37": {
"class_type": "SaveImage",
"inputs": {
"filename_prefix": "ComfyUI-image_upscaled",
"images": [
"35",
0
]
},
"_meta": {
"title": "Save Upscaled Image"
}
},
"40": {
"class_type": "PrimitiveStringMultiline",
"inputs": {
"value": "high-contrast studio lighting"
},
"_meta": {
"title": "Describe the Lighting"
}
},
"41": {
"class_type": "RegexReplace",
"inputs": {
"case_insensitive": True,
"count": 0,
"dotall": False,
"multiline": False,
"regex_pattern": "{lighting}",
"replace": [
"40",
0
],
"string": [
"28",
0
]
},
"_meta": {
"title": "Replace Text (Regex)"
}
},
"19:10": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"19:16",
0
],
"image": [
"1",
0
],
"width": [
"19:15",
0
],
"x": 0,
"y": [
"19:12",
0
]
},
"_meta": {
"title": "3"
}
},
"19:11": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"19:16",
0
],
"image": [
"1",
0
],
"width": [
"19:15",
0
],
"x": [
"19:13",
0
],
"y": [
"19:12",
0
]
},
"_meta": {
"title": "4"
}
},
"19:12": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"19:16",
0
],
"b": [
"19:14",
0
],
"value": "a + b"
},
"_meta": {
"title": "Row 2 - Y val"
}
},
"19:13": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"19:15",
0
],
"b": [
"19:14",
0
],
"value": "a + b"
},
"_meta": {
"title": "Col 2 - X val"
}
},
"19:14": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"18",
0
],
"b": 0,
"value": "a*2"
},
"_meta": {
"title": "🔧 Simple Math"
}
},
"19:15": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"19:17",
0
],
"b": [
"19:14",
0
],
"value": "(a-2*b) / 2"
},
"_meta": {
"title": "WIDTH"
}
},
"19:16": {
"class_type": "SimpleMath+",
"inputs": {
"a": [
"19:17",
1
],
"b": [
"19:14",
0
],
"value": "(a-2*b) / 2"
},
"_meta": {
"title": "HEIGHT"
}
},
"19:17": {
"class_type": "GetImageSize",
"inputs": {
"image": [
"1",
0
]
},
"_meta": {
"title": "Get Image Size"
}
},
"19:19": {
"class_type": "ImageBatchMulti",
"inputs": {
"image_1": [
"19:8",
0
],
"image_2": [
"19:9",
0
],
"image_3": [
"19:10",
0
],
"image_4": [
"19:11",
0
],
"inputcount": 4
},
"_meta": {
"title": "Image Batch Multi"
}
},
"19:4": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"19:8",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"19:5": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"19:9",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"19:6": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"19:10",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"19:7": {
"class_type": "PreviewImage",
"inputs": {
"images": [
"19:11",
0
]
},
"_meta": {
"title": "Preview Image"
}
},
"19:8": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"19:16",
0
],
"image": [
"1",
0
],
"width": [
"19:15",
0
],
"x": 0,
"y": 0
},
"_meta": {
"title": "1"
}
},
"19:9": {
"class_type": "ImageCrop",
"inputs": {
"height": [
"19:16",
0
],
"image": [
"1",
0
],
"width": [
"19:15",
0
],
"x": [
"19:13",
0
],
"y": 0
},
"_meta": {
"title": "2"
}
}
})
# Replace each example.com URL with a public URL of your file.
workflow.set_input("2", "image", client.assets.from_url("https://example.com/replace-with-a-url/image"))
# 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 LoadImage node supports common image formats such as JPEG, PNG, and TIFF for uploading your poster design.
You can install the required custom nodes by following the guide available at the ComfyUI documentation or visiting the respective GitHub repositories linked in the author notes.
Currently, the scenes are generated based on pre-defined settings in the workflow. Customization options may be added in future updates.
Ensure that your input design is properly formatted and that the brand description is clear. You may also want to adjust the image size settings in the workflow.