This ComfyUI workflow demonstrates how to guide MOSS Image generation with a Color-Depth Control LoRA trained by darask0. It lets you steer structure with a depth map while preserving or modulating color from a reference image, making it easy to create style variations that keep geometry intact. At its core, LoadImage brings in your RGB reference and (optionally) a precomputed depth map, while PreviewImage and ImageCompare help you iterate quickly and check differences across runs. SaveImageAdvanced handles clean, configurable exports.
The heavy lifting happens in two custom MOSS Image nodes (IDs: 3d58c0bd-7a87-40a7-bdb5-a209a27f10d7 and 882218ff-0ea7-4047-9b12-b79cdc4e9faa). Together, they wrap the MOSS Image base model and apply the Color-Depth Control LoRA at inference, injecting depth-aware conditioning so the output adheres to your scene layout and perspective. By pairing color information from the source image with structural cues from the depth map, the workflow produces edits and variations that maintain shape, scale, and composition—ideal for product shots, architecture, and concept iterations where consistency matters.
API
通过代码使用此工作流
每个 Comfy 工作流都是一个 JSON 图。下面的内容就是此工作流的完整数据，ComfyUI 运行时也是如此——你可以从该 URL 获取、放入版本控制，或在 ComfyUI 中加载并逐节点运行。
使用 Comfy SDK 从 TypeScript 或 Python 运行。相同代码可用于 Comfy Cloud 或你自托管的 ComfyUI——只需更改基础 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 需要 API 格式的工作流：在 ComfyUI 中打开此工作流，使用 文件 → 导出工作流（API）。
