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
Use este workflow a partir do código
Todo workflow Comfy é um grafo JSON. O payload abaixo é este workflow, exatamente como o ComfyUI executa — obtenha-o pela URL, mantenha-o no controle de versão ou carregue-o no ComfyUI e execute nó por nó.
Execute a partir de TypeScript ou Python com o Comfy SDK. O mesmo código serve para o Comfy Cloud ou um ComfyUI hospedado por você — apenas a URL base muda.
// 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");O SDK utiliza um workflow no formato API: abra este workflow no ComfyUI e use Arquivo → Exportar Workflow (API).
O acesso à API do Comfy Cloud requer um plano com chave de API.
