🧩Seedream-v5-pro图层拆分@炮老师的小课堂

If you've ever tried to cut a subject out of a generated image for compositing, you know the drill: background-removal models give you one alpha, and that's it. DapaoSeedreamV5ProLayerDecompositionNode ("🧩Seedream-v5-pro图层拆分@炮老师的小课堂") is different - it calls ByteDance's seedream-v5-pro/layer-decomposition endpoint and asks the model to actually understand the image's structure. One image goes in; you get the background plus up to 16 transparent layers out, which is the difference between "a cutout" and "a workable layered file."

What it does

The endpoint splits a single image into a base background and individually separated subject layers with alpha - think of it as a very smart, very literal interpretation of "decompose this scene into its pieces." You get the layers as an IMAGE batch, their corresponding MASK batch, and even a "PSD" layer batch for stacking back together. That's a genuinely useful capability for character cards, merch mockups, or any compositing workflow that used to mean hours of manual masking.

Inputs that matter

• 🖼️ 待拆分图像 - the image to decompose. Tooltip is firm: exactly one image, 512–6000px, up to 30MB.
• 📝 拆分要求 - natural language: "split out the person and the coffee cup" - or a structured <bbox>x1 y1 x2 y2</bbox> bounding-box tag if you want to force specific regions. Coordinates are 0–1000.
• 🧩 清晰度 - 自动 / 1K / 1.5K / 2K.
• 🧠 拆分优化 - 标准模式 or 快速模式.
• 📦 基础图格式 - JPEG or PNG for the background. The transparent layers are always PNG, regardless.
• 🎲 随机种 - the layer endpoint has no seed; cache control only.

Outputs

Six of them: 🖼️ 基础背景图 (the clean background), 🧩 透明图层批次 (IMAGE batch - each subject as its own alpha layer), 🎭 图层蒙版批次 (MASK batch matching those layers), 🔗 结果链接, 📋 图层信息 (what each layer is), and 🗂️ PSD透明图层批次 (the same layers arranged for reassembly). If you're building a compositing graph, the IMAGE and MASK batches are the pair you'll wire onward.

Install and expectations

Standard pack install (Manager → search dapaoAPI, or clone + pip install -r requirements.txt + restart) and a dapaoAI API key. No local models.

Set expectations before you spend: this is a cloud call against a very new API endpoint, and layer decomposition is only as smart as the model's understanding of your image. Clean, well-separated subjects work great; a crowded scene with overlapping elements will produce layers that need cleanup - the endpoint is impressive, not magic. Also note the 404 branch in the source is handled: if your relay hasn't configured this model yet, you get a clear "中转站没有配置 layer-decomposition" error rather than a cryptic failure. It's the sort of node that's either exactly what your workflow needs or a fun toy you run twice - but for anyone doing layered asset work from generated images, it's the only tool of its kind in this pack, and arguably the most novel one in it.