Do I need a mask to limit the edit area?

No mask is required for this template. OpenAIGPTImageNodeV2 performs semantic, instruction‑based edits that aim to preserve the rest of the image. If you need pixel‑precise masked edits, consider an inpainting workflow that includes mask nodes.

How do I ensure the pose and background don’t change?

Be explicit in your prompt about what must remain unchanged (e.g., “Keep the same pose, background, lighting, and framing”). Keep requested changes small and focused. Reusing the same base image and consistent phrasing across runs helps maintain fidelity.

What image size should I use?

Start with a moderate resolution that the API handles reliably (for example, around 1024 on the long side). Extremely large images may be resized by the API. If the node allows selecting output size, pick one that matches your use case and hardware/API limits.

Why do results vary between runs?

Text‑guided editing can introduce variation. Keep prompts specific, avoid contradictory instructions, and change only one variable at a time. If the node supports multiple variations, compare them and iterate toward your target look.

GPT Image 2.5 Sunburst: Image Edit

This ComfyUI template demonstrates a high‑fidelity image editing pipeline that preserves the original subject, pose, lighting, and composition while making targeted changes. It’s built around a simple, reliable graph: LoadImage feeds a reference image into OpenAIGPTImageNodeV2 for natural‑language edits, and SaveImageAdvanced handles consistent output naming and metadata. The included sample asset (hairstyle_makeup_base_gpt_image25.png) lets you test the flow immediately.

Technically, OpenAIGPTImageNodeV2 calls an OpenAI GPT image model in image‑edit mode, using the source pixels as strong conditioning so that only requested elements (e.g., outfit, hairstyle, facial features, or camera angle) change. You guide the edit by writing a precise prompt that also states what must stay the same (for example: “Keep the same pose, lighting, background, and composition; only change the hairstyle to a short bob”). This makes the workflow excellent for sequential frames and subject‑consistent variations. SaveImageAdvanced then writes the results with versioned filenames so iterations are easy to track.

API

Use this workflow from code

Every Comfy workflow is a JSON graph. The payload below is this workflow, exactly as ComfyUI runs it — fetch it from the URL, keep it in version control, or load it in ComfyUI and run it node by node.

f423e4de5251.json

Fetching workflow JSON…

Run it from TypeScript or Python with the Comfy SDK. The same code targets Comfy Cloud or a ComfyUI you host yourself — only the base URL changes.

// 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");

The SDK takes a workflow in API format: open this workflow in ComfyUI and use File → Export Workflow (API).

Comfy Cloud API access requires a plan with an API key.

SDK docs Get an API key

FAQ

Frequently Asked Questions