Aseprite VLM Audit Prompt

Diffusion models are great at spritesheets and terrible at obeying your exact frame grid. GameAssets_AsepriteVLMAuditPrompt is the pack's answer to that: it builds a strict, JSON-only prompt for a vision-language model, telling it to inspect your generated sheet and correct the Aseprite frame rectangles. The output isn't the correction - it's the prompt, so you can feed it to whatever VLM you already use.

This is part of comfyui-game-assets-maker, the pack quinteroac shared on r/comfyui as a playground for GPT Image 2 sprite pipelines. This audit-prompt + correction pair is the most "automation is real" bit of the pack, and it works with any VLM node that takes image + text and returns text.

How it works

You connect the spritesheet IMAGE and the atlas JSON (from Aseprite Visual Novel Atlas or Aseprite Animation Atlas). The node inspects the atlas, decides what kind it is, and writes a prompt that instructs the VLM to output corrected frame rectangles as strict JSON - no prose, no markdown, so the result is parseable downstream. It also passes the image straight through so both land in your VLM node.

atlas_kind lets you force it: Auto (guesses from the JSON - animation atlas if it has frameTags, otherwise visual novel), Visual Novel, or Animation. If your atlas is ambiguous, set it explicitly. image_index picks which image in the batch is the sheet.

What you wire where

The two outputs are spritesheet (the passthrough IMAGE) and vlm_prompt (the STRING). Both go into your VLM node: image + text in, text response out. That response then feeds into Aseprite Apply VLM Correction, which is the node that actually applies the rectangles back to the atlas. The report output tells you what kind of atlas it detected and how many frames it's auditing.

Installing

ComfyUI Manager (search "comfyui-game-assets-maker") or:

cd ComfyUI/custom_nodes
git clone https://github.com/quinteroac/ComfyUI-GameAssetsMaker.git
cd ComfyUI-GameAssetsMaker
pip install -r requirements.txt

Restart after. The pack itself only needs opencv-python - the VLM is your choice and your model.

Where people get burned

The name is a lie in the best way: this node calls no API and needs no key. It only builds text. So if nothing happens after you run it, that's expected - you still have to connect a real VLM (Qwen, GPT Image, whatever you've got) and send the prompt. And pick a VLM that follows instructions; the whole value is a parseable JSON response, and a chatty model defeats it. If you get prose back, the correction node will struggle with it.