HyprLab ImageGen API Node

This node is the fastest way to get Google's "Nano Banana" image model inside a ComfyUI workflow without touching the Gemini app or Google's own API. You pay a small third-party host called HyprLab per image instead, and the results drop straight out as a normal ComfyUI IMAGE tensor. If you've seen the nano-banana image-edit demos that were all over r/StableDiffusion - the "put her in the car with the same pose" crowd - this is the closed-source model behind them, served through an OpenAI-style endpoint.

The catch is upfront: nothing about this runs locally. No model download, no VRAM, no GPU at all. The node is a thin HTTP wrapper. You need a HyprLab account and an API key, and every image you generate is a paid API call, so this is a "some money, no compute" trade, not a free local workflow.

How it works

The node POSTs a JSON payload to https://api.hyprlab.io/v1/images/generations with your key as a Bearer token. The payload carries the model, prompt, resolution, aspect ratio, seed, and asks for response_format: b64_json. When the response comes back it decodes the base64 into a PIL image and converts it to a tensor - so unlike a lot of API wrappers in this pack, you don't need a conversion node after this one. Wire IMAGE straight into Preview Image or Save.

Two niceties worth knowing. First, it retries on flaky connections: three attempts with exponential backoff between 5 and 30 seconds, only for connection errors and timeouts (not for a 401 or a 400). Second, if you feed it an image_input, the node downscales your image to about 1.5 megapixels before base64-encoding it, which keeps uploads fast. That input path is where Nano Banana genuinely shines - it's an editing model, not just a text-to-image one.

The inputs that matter

You'll actually touch maybe five of these:

• api_key - the one that gates everything. Get it from your HyprLab account and paste it in. Default is literally YOUR_API_KEY_HERE, so the first run will 401 until you set this.
• prompt - the main prompt. It's set as forceInput, so you can wire it from another node (say, the output of an LLM node in this same pack).
• model - defaults to nano-banana-pro. Leave it alone unless your HyprLab plan lists a different model name; using one you don't have is the single most common model error.
• resolution - 1K / 2K / 4K. 4K will cost you more and take longer; start at 1K.
• aspect_ratio - eleven choices from 1:1 through 21:9, plus match_input_image, which is the right pick when you're editing an existing image.
• seed - an integer, default 0. Set it to reproduce a result; the API should honor it.

The one optional input, image_input, takes any IMAGE tensor - from Load Image or the pack's own Load Image from URL - and sends it along as a reference for guided generation or editing.

Installing it

It ships in the ComfyUI_BillBum_Nodes pack, and all four nodes in this set install together. Easiest path: ComfyUI Manager, search for billbum, install, restart. Or clone it yourself:

cd ComfyUI/custom_nodes
git clone https://github.com/AhBumm/ComfyUI_BillBum_APIset_Nodes.git
pip install -r requirements.txt   # tenacity, openai, pillow, requests, numpy, tiktoken, urlextract

Dependencies are all lightweight and there are no model files to download - the whole pack is API calls and string utilities. Just restart ComfyUI after installing.

Troubleshooting

• 401 / auth errors - key is wrong, missing, or the api_url got mangled. Check the key in your HyprLab console, and only change the URL if you know what you're doing.
• Model errors - the model you picked isn't available on your plan. This is the pack README's own first bit of advice: make sure model, base URL, and provider actually agree.
• "API did not return any valid images" - the node prints the raw API response to the console before failing; read that, it tells you what the server actually said.
• It cost money - there's no free tier surprise here; confirm the plan and price before running a batch. A 4K edit run adds up fast.

Fair warning: this is a small, community-maintained pack (the HyprLab node barely registers in search traffic), so expect rough edges. If the endpoint shape changes, the README and the nodes4hypr.py source are where the truth lives.