Andrew Adams · Co-Founder & Operations at Wireflow · Leonardo AI API
Searching for the Leonardo AI API usually means you want images your product can order on demand.
The workflow on this page does exactly that: a Text Input wired into Nano Banana 2, published as one REST endpoint your code calls.
Free to build · no credit card
At Wireflow, Andrew and the team have built and iterated on 300+ leonardo api workflows for creative teams and agencies. The approach below reflects what we've found delivers the most consistent, production-ready results.
An honest answer to the Leonardo AI API search
Leonardo runs its own API for its own models. If you specifically need those models, that API is the right tool, and Wireflow will not pretend otherwise: it does not host or resell Leonardo's models, and the two platforms are not affiliated.
What most teams typing this search actually need is programmatic image generation they control: a strong model, callable from code, swappable when a better one ships. That is what the workflow above is. A Text Input wired into Nano Banana 2 makes a working AI image generator, and publishing it turns the graph into the API your product calls. You iterate on the canvas; your codebase keeps one URL.
What the image workflow API can do
70+ hosted models
Nano Banana 2, Flux 2 Pro, Seedream V4.5, GPT Image 2, Recraft V4, and Ideogram V4 all run as canvas nodes.
Reference image ports
Nano Banana 2 takes two optional images beside the prompt, so products and characters stay consistent.
Chained post-processing
Wire Topaz Upscale or a background remover after the model; the whole chain answers one API call.
Batch over a feed
Loop one workflow across a CSV or product feed and render every row through the same graph.
Canvas-first testing
Dial in prompts and settings visually, then publish; the endpoint serves exactly the graph you tested.
REST and MCP built in
Every published workflow is an endpoint and an MCP tool with typed inputs and asset URLs back.
Why the model node matters more than the model
Image models age in months. The model that wins on photorealism today loses to a release that ships next quarter, and an integration welded to one vendor's API inherits that decay. On a canvas the model is one node in a graph: unplug Nano Banana 2, drop in Flux 2 or GPT Image 2, run the same prompt, and keep whichever wins.
Reproducibility is what makes the swap safe. Workflows are versioned server-side, so the five hundredth render walks the same graph as the first, and a model change is a deliberate new version instead of silent drift. The honest tradeoff: every generation spends credits, so a batch over a large feed is a budgeting decision, and building the graph itself costs nothing.
When Leonardo's own API is the better pick
If your product depends on Leonardo's specific models or the tooling built around them, use Leonardo's API; those models live only there, and no canvas layer changes that. Wireflow also is not a wrapper that proxies other vendors' private APIs, and it does not run offline or support custom Python nodes.
Wireflow earns its place when the job is bigger than one fixed model: when you want to test prompts visually before they hit production, chain generation into upscaling or background removal, keep one endpoint while models rotate underneath it, or hand the whole workflow to an agent as an MCP tool. If that is the shape of your problem, the two-node flow above is the smallest honest start.
More Than Just Leonardo AI API
A prompt in, a 2K image out
One visible graph wires Text Input to Nano Banana 2 at 2K square, with no orchestration or queue plumbing.
Models are nodes, not contracts
Swap Nano Banana 2, Flux 2 Pro, Recraft V4, or GPT Image 2 on the REST canvas; your app keeps one URL.
Reference images are input ports
Nano Banana 2 has two optional image inputs. Reuse a product shot as a reference for image edits via API.
One flow, a whole catalog
Call the published API once per feed row; each render repeats the same graph for reproducible batch image generation.
Grow the graph, keep one call
Add Topaz Upscale or background removal after the model, republish, and keep the same programmatic image API call.
Build Any AI Workflow
AI Models Integrated
Full Commercial License
FAQs
Generating images programmatically with Leonardo's hosted models: developers send prompts over HTTP and get images back. If you need those specific models, use Leonardo directly; Wireflow is a separate platform and does not host them.
More From Wireflow
Written by
Andrew Adams · Co-Founder & Operations at Wireflow
Runs client operations and content strategy at Wireflow. Works directly with creative teams and agencies to build production AI workflows.
Open the two-node flow behind this page
It is live on the canvas: a Text Input wired into Nano Banana 2 at 2K. Run a prompt, watch the render land on the node, then publish your own copy as the endpoint your code calls. Building is free; generations are pay per run.