This ComfyUI tutorial walks you through a simple, reliable product-image upscaling pipeline built around three nodes: LoadImage, GeminiImage2Node, and SaveImage. You start by loading your product photo, pass it to GeminiImage2Node configured to use the Nano Banana Pro model, and then save the enhanced result. The Nano Banana Pro model is tuned for product imagery—reinforcing edges, materials, and label regions—so you get cleaner type, crisper contours, and more believable surface detail at a higher resolution.
Technically, GeminiImage2Node serves as an API-backed image-to-image processor. It takes the input from LoadImage, requests an upscaled, detail-enhanced version from the Nano Banana Pro model, and returns a refined output while aiming to maintain color and structure. Because the heavy lifting is done via API, the workflow stays lightweight inside ComfyUI: no local checkpoints to manage, consistent outputs between runs (given the same settings), and straightforward saving through SaveImage to your preferred format and path.
API
通过代码使用此工作流
每个 Comfy 工作流都是一个 JSON 图。下面的内容就是此工作流的完整数据，ComfyUI 运行时也是如此——你可以从该 URL 获取、放入版本控制，或在 ComfyUI 中加载并逐节点运行。
使用 Comfy SDK 从 TypeScript 或 Python 运行。相同代码可用于 Comfy Cloud 或你自托管的 ComfyUI——只需更改基础 URL。
// Install (beta)
npm i @comfyorg/sdk
// Run this workflow (TypeScript)
import { Comfy } from "@comfyorg/sdk";
const client = new Comfy({ apiKey: "comfyui-..." });
const wf = await client.workflows.fromFile("workflow_api.json");
const job = await client.run(wf);
await job.getOutputs("<output-node-id>")[0].toFile("output.png");SDK 需要 API 格式的工作流：在 ComfyUI 中打开此工作流，使用 文件 → 导出工作流（API）。
