惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

WordPress大学
WordPress大学
小众软件
小众软件
MongoDB | Blog
MongoDB | Blog
Hugging Face - Blog
Hugging Face - Blog
Jina AI
Jina AI
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Stack Overflow Blog
Stack Overflow Blog
L
LangChain Blog
大猫的无限游戏
大猫的无限游戏
量子位
A
About on SuperTechFans
G
Google Developers Blog
雷峰网
雷峰网
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
IT之家
IT之家
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园_首页
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Vercel News
Vercel News
V
Visual Studio Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 聂微东
U
Unit 42
Apple Machine Learning Research
Apple Machine Learning Research

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
How I Decompose Any Image Into Recomposable Layers on Melius
Igor Gridel · 2026-05-20 · via DEV Community

When you iterate on an AI-generated image by re-prompting, you destroy it.

You ask the model to "move the figure left and remove the truck," and the entire scene shifts: different lighting, different building geometry, different snow texture. Anything that wasn't pinned by the original seed gets re-rolled. You lose the bake you liked.

Photographers don't work this way. Concept artists don't work this way. They hold the layers and move them.

I wanted that. So I built it.

The pipeline in one paragraph

One Melius canvas. An analyzer LLM reads any input image and outputs a JSON blueprint of seven candidate layers: background, middle ground, foreground, and up to four subjects. Seven parallel extractor LLMs pull each layer's isolation prompt out of the JSON. Seven NanoBanana Pro nodes regenerate each layer on chroma green, preserving original position, scale, and lighting. Background-removal nodes strip the green. A final NanoBanana Pro pass takes all seven transparent layers and renders a unified, lighting-coherent composition. You can move, scale, drop, or swap any layer before the final pass.

Why this matters

Single-pass generation treats an image as one indivisible thing. Inpainting and reference conditioning are partial fixes, but the model still guesses at occluded regions. Move the truck and the snow underneath has to be invented from nothing.

The fix is to never bake the image to begin with. Treat the original as a brief, decompose it into stacked elements, and only recompose at the end, after the elements are arranged the way you want them.

That requires a real pipeline. A node graph is the right shape for a real pipeline. Melius gave me the canvas that can hold one.

What's in the full breakdown

I wrote the full workflow on Scopeful Pro: every node, both system prompts (the analyzer and the universal extractor template), the mistakes I made first, and the things I'd change in v2.

If you want the prompts, the node-by-node walkthrough, and the canvas itself, here is the full version:

How I Decompose Any Image Into Recomposable Layers on Melius

The video demo (14 seconds, the layer explode shot) is on my X feed (@igorgridel).