What exactly does AILab_QwenVL do in this workflow?

AILab_QwenVL runs a Qwen-VL vision-language model on the input from LoadImage. It takes your instruction (prompt) plus the image, then generates text—such as a caption, list of objects, or an answer to a question about the image—which PreviewAny displays.

Do I need an internet connection or API key to run this?

It depends on how AILab_QwenVL is configured in your environment. Many setups load local model weights and run fully offline. If your node is configured to call a remote backend, you may need network access or credentials. Check your node’s model selection and documentation.

How can I control the style and length of the output?

Guide the model with explicit instructions (e.g., "One-sentence caption," "Return keywords separated by commas," or "Output JSON with fields: setting, subjects, actions"). If your node exposes settings like maximum tokens or temperature, use them to limit length and make results more deterministic.

How do I get more accurate results (especially for text in images)?

Use clear, specific prompts (e.g., "Transcribe all legible text exactly, line by line"), provide sufficiently large, sharp images, and reduce temperature for factual tasks. If the scene is busy, crop to the relevant area before running LoadImage so the model focuses on what matters.

AI on the Lot 2026 - QwenVL - i2t

This workflow turns any image into descriptive, instruction-following text using Qwen-VL inside ComfyUI. It’s a simple, reliable chain: LoadImage brings your source frame or photo into the graph, AILab_QwenVL runs the Qwen-VL vision-language model to generate captions or answers based on your instruction, and PreviewAny displays the resulting text directly in the UI. Because AILab_QwenVL accepts both an image and a prompt, you can steer the output toward concise alt text, detailed scene breakdowns, tags, or even OCR-style transcriptions.

Technically, AILab_QwenVL encodes the visual content and decodes text conditioned on your instruction (e.g., "Describe this scene in one sentence" or "List objects with counts"). You can typically adjust generation behavior (such as temperature or maximum output length, if exposed by your node build) to balance creativity vs. precision. The minimal node graph keeps it fast and teachable—ideal for creating consistent captions, production notes, or metadata without extra post-processing nodes. PreviewAny then renders the string output so you can copy it straight from ComfyUI.

API

Use this workflow from code

Every Comfy workflow is a JSON graph. The payload below is this workflow, exactly as ComfyUI runs it — fetch it from the URL, keep it in version control, or load it in ComfyUI and run it node by node.

GET https://comfy.org/workflows/download/231992fee1a6.json

Fetching workflow JSON…

Run it from Python or TypeScript with the Comfy SDK. The same code targets Comfy Cloud or a ComfyUI you host yourself — only the base URL changes.

# Install (beta)
pip install comfy-sdk        # Python
npm i @comfyorg/sdk          # TypeScript

# Run this workflow (Python)
from comfy_sdk import Comfy

client = Comfy(api_key="comfyui-...")
wf = client.workflows.from_file("workflow_api.json")
job = client.run(wf)
for output in job.get_outputs("<output-node-id>"):
    output.to_file(output.name)

The SDK takes a workflow in API format: open this workflow in ComfyUI and use File → Export Workflow (API). API access requires a Comfy Cloud plan with an API key. SDK docs · Get an API key

FAQ

Frequently Asked Questions

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