LoRA Test Prompt Generator

You just finished training a LoRA. Now comes the part nobody loves: figuring out whether it actually works. The honest answer requires more than one render - a single pretty image proves nothing, because a person-LoRA can ace a headshot and fall apart on a full body, and a style-LoRA can nail one scene type and wander off on the next. LoRA Test Prompt Generator is a two-second way to get ten deliberately varied prompts that probe different failure modes at once.

It's the "validation" step in the training loop that the KB's LoRA docs keep circling: dataset and captions decide most of training quality, but the moment of truth is what comes out the other side. This node is that moment, automated.

Inputs

• trigger_word - the token your LoRA responds to. It gets inserted into every prompt at the right spot (for subject/product/vehicle LoRAs it's the subject; for style LoRAs it's appended as the style descriptor).
• lora_type - the four-way switch that picks the prompt set: subject_person, style, product, vehicle.
• seed - reserved for future randomization; leave it.
• quality_suffix - optional tags appended to every prompt, e.g. 8k, detailed. Good for matching your training's quality token vocabulary.

What the prompts actually test

The craft here is the variety, and it's worth reading a few:

• subject_person: a headshot, a sunlit cafe candid, a dark Rembrandt close-up, a low-angle rooftop full body, a laughing mid-close-up, a side profile with rim light, a walking shot with shallow DOF, an f/1.4 eye-texture macro, a 1970s film-grain medium, a neon-lit night street. Lighting, angle, framing, expression - the four axes where person-LoRAs most often collapse.
• style: ten completely different subjects - an old man's face, a mountain lake, a cathedral interior, a flower still life, a knight vs dragon, a lone figure on a bench, a howling wolf, a rainy Tokyo alley, a bioluminescent forest, a wet apple. If your style holds across a face, a landscape, a beast, and a still life, it's a real style, not a prompt-shaped accident.
• product and vehicle: white-background e-commerce, in-hand lifestyle, macro texture detail, hero shots, environmental context - the contexts a product actually gets photographed in.

So a single batch of ten tells you not just "does it work" but where it works, which is how you decide whether to re-train or just learn the trigger's limits.

Outputs

prompt_1 through prompt_10 - individual STRING outputs you can wire to ten CLIP text encoders, or iterate with a switch - plus all_prompts, a single multi-line string for batch-mode workflows that feed a prompt-per-line loader. The all_prompts output is the one you'll actually use: pipe it into a text-file writer or a batch loader and render all ten at once.

Install

Ships in TrentNodes:

cd ComfyUI/custom_nodes
git clone https://github.com/TrentHunter82/TrentNodes.git
cd TrentNodes
pip install -r requirements.txt

Restart, find it under TrentNodes/Testing. It's pure string generation - no models, no GPU.

The one caveat

These are static templates, and they're tuned for SD-family prompt syntax. A Flux or Wan LoRA that uses natural-language prompting may want different phrasing, though the trigger insertion still works. And remember the golden rule of LoRA testing: use the same seed across all ten, then vary the seed and rerun - one batch proves consistency, two batches with different seeds prove the model, not the luck. The quality suffix is your lever for matching how you trained; leave it empty if you trained without quality tags, or your test results will be polluted by vocabulary the LoRA never learned.