GALIAIS-Nodes AI Natural Prompt Writer

Some 2026 checkpoints read natural language better than a comma-separated tag list - especially the ones with an actual LLM text encoder where your prompt is an instruction, not a token bag. The AI Natural Prompt Writer is this pack's bridge across that gap: you feed it structured tag analysis, it hands your language model the task "write a natural-language positive prompt from these analyzed Danbooru tags," and you get a sentence-based prompt that plays nice with models that prefer prose. It's the LLM-in-the-graph pattern, done narrowly: one job, no drift.

What you feed it

• AI服务商 - the provider object from GaliaisNodesAIProvider. This is the node that makes the actual API call, so if you haven't configured a provider yet, this won't run. Note the Chinese label: "AI服务商" = AI service provider.
• Tag分析JSON - the structured analysis from the GaliaisNodesAITagAnalyzer. It's meant to be wired in, not typed, and the analyzer's 分析JSON output is the intended source.
• 输出语言 (output language) - 中文, 英文, or 中英混合 (Chinese-English mixed). Defaults to English.
• 细节强度 (detail intensity) - 精炼 (concise), 标准 (standard), or 详细 (detailed). Standard is a safe default; 详细 leans wordy and 精炼 stays lean.

Two outputs come back: 自然语言提示词 (the natural-language prompt) and 原始响应JSON (the raw API response). The first is what you wire into a positive-prompt encoder or further up the composition chain; the second is for debugging and keeping an audit trail of exactly what the model said.

The realistic take

A few honest things before you get excited. First, every run is a live API call with your key and your money, so a long iteration session spends tokens. Second, an LLM that writes prompts will occasionally embellish - that's the subject-drift failure mode people hit with any prompt-enhancer node. Because this one is constrained to a single task and consumes pre-analyzed tags rather than free text, it drifts less than an open-ended enhancer, but it's still a language model. Check the 原始响应JSON if output ever looks off, and keep your tag input clean rather than fighting the writer's output.

Where it shines: switching a tag-heavy character build into prose for a model like Anima or a T5-driven base without hand-writing the sentence. If you're on a pure tag-model (Illustrious, NoobAI), honestly, skip this node - the tags were already what that model wants, and the extra API call buys nothing.

Install and gotchas

Same one-time pack install:

git clone https://github.com/GALIAIS/GALIAIS-Nodes.git ComfyUI/custom_nodes/GALIAIS-Nodes

Restart ComfyUI, or use ComfyUI Manager and search "GALIAIS-Nodes". No pip dependencies, no bundled models. The only gotchas are the external-API ones: it needs a working provider (run the AI Provider Health Check first), and the pack is young with no community writeups yet, so the README's recommended flow and the JSON outputs are your best guides. If output language comes out wrong, you picked the wrong 输出语言, not the wrong model.