The Qwen-Image InstantX Union ControlNet workflow is designed to generate high-quality images using the Qwen-Image model with advanced ControlNet capabilities. This workflow integrates multiple control techniques such as canny edge detection, soft edge, depth, and pose estimation to offer fine-grained control over the image generation process. By leveraging these techniques, users can create images that adhere closely to specific visual constraints or stylistic guidelines. Technically, the workflow utilizes a combination of nodes including SaveImage, LoadImage, and PreviewImage to handle image data, while specialized nodes like 0e306186-0dda-4b49-bfcb-98cab32901d9 and f8e756aa-94b4-4c02-943d-2e7910344622 are used to apply the ControlNet transformations. The FluxKontextImageScale node further refines the scaling of images, ensuring that the output maintains high fidelity to the input parameters.
API
從程式碼使用此工作流
每個 Comfy 工作流都是一個 JSON 圖。下方的 payload 就是此工作流，與 ComfyUI 執行時完全相同 — 你可以從此 URL 取得、放入版本控制，或在 ComfyUI 載入並逐節點執行。
使用 Comfy SDK 以 TypeScript 或 Python 執行。相同程式碼可用於 Comfy Cloud 或你自架的 ComfyUI — 只需更改 base URL。
// Install (beta)
npm i @comfyorg/sdk
// Run "Qwen-Image InstantX ControlNet" (TypeScript)
import { Comfy } from "@comfyorg/sdk";
const client = new Comfy({ apiKey: "comfyui-..." });
// This workflow, exported in API format (see note below)
const wf = await client.workflows.fromFile("image_qwen_image_instantx_controlnet_api.json");
const asset = client.assets.fromFile("input.png");
wf.setInput("71", "image", asset); // LoadImage
const job = await client.run(wf);
await job.getOutputs("60")[0].toFile("output.png"); // SaveImageSDK 需使用 API 格式的工作流：請在 ComfyUI 開啟此工作流，並使用 檔案 → 匯出工作流（API）。
常見問題
