This workflow compares how different garment image types—flat-lay, mannequin, and on-model—perform in a virtual try-on using the Kling model. Two LoadImage nodes bring in a person photo and a garment image; the KlingTryOnNode then generates a realistic try-on while preserving the subject’s face and pose. The ImageCompare node assembles a side-by-side view of the original person photo and the generated try-on so you can visually judge fit alignment, drape plausibility, color/pattern fidelity, and identity preservation. SaveImageAdvanced stores both the try-on and the comparison output, and a MarkdownNote provides in-graph guidance.
To make this a fair comparison, keep the same person image while swapping the garment source across flat-lay, mannequin, and on-model product shots. Normalize garment framing and scale before each run so the KlingTryOnNode sees similar inputs. Expect trade-offs: flat-lay images are clean and easy to segment but offer fewer 3D cues; mannequin shots add structure without pose conflict; on-model shots carry natural drape cues but may introduce pose/shadow mismatches. The side-by-side ImageCompare output helps you quantify which input style yields the fewest artifacts for your catalog or campaign.
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 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）。
