Multi Model AI Workflow

A multi model AI workflow chains two or more AI models together so the output of one feeds into the next. For example, a text prompt generates an image, then a video model animates that image, all in one automated pipeline.

Wireflow supports image generators like Nano Banana 2, Recraft V4, and Flux 2 Pro, video models like Kling Video, and post-processors like ClarityAI upscaler and BiRefNet background remover. Any combination works on the canvas.

No. Wireflow uses a visual drag-and-drop canvas. You connect nodes with edges and the platform handles data passing, API calls, and execution order automatically.

There is no hard limit on the number of nodes. Most practical workflows use two to four models, but you can build longer pipelines for complex production needs.

Yes. A common pattern is text-to-image followed by image-to-video. The image output connects directly to the video model's input frame, creating a seamless generation pipeline.

Wireflow stops execution at the failing node and shows the error. You can fix the input or swap the model and re-run from that point without restarting the entire workflow.

Yes. Every workflow saves automatically. You can duplicate it, share it via link, or use it as a template for similar projects.

They describe the same concept. Multi model workflow emphasizes the visual pipeline approach, while model chaining focuses on the sequential connection between models. Wireflow supports both perspectives on its canvas.