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
Usa este flujo de trabajo desde código
Cada flujo de trabajo de Comfy es un grafo JSON. El siguiente payload es este flujo de trabajo, exactamente como lo ejecuta ComfyUI: descárgalo desde la URL, guárdalo en control de versiones o cárgalo en ComfyUI y ejecútalo nodo por nodo.
Ejecuta desde TypeScript o Python con el Comfy SDK. El mismo código funciona en Comfy Cloud o en un ComfyUI propio: solo cambia la URL base.
// 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");El SDK utiliza un flujo de trabajo en formato API: abre este flujo de trabajo en ComfyUI y usa Archivo → Exportar flujo de trabajo (API).
El acceso a la API de Comfy Cloud requiere un plan con clave API.
