Ep 4: Text-to-Image from scratch



A complete text-to-image workflow. No template. You'll place every node and understand why each one is there.

A text-to-image workflow does five things:

Start here. The KSampler is where generation happens. Everything else feeds into it. You'll see four inputs on the left: model, positive, negative, and latent image.

Add a Load Diffusion Model node. Connect its violet output to the KSampler's violet model input. This gives the KSampler the AI model it needs to generate.





Add two CLIP Text Encode nodes.

Both need a CLIP model. Add a Load CLIP node and connect it to both. Make sure the CLIP model matches your diffusion model.

Add an Empty Latent Image node. Set width and height (1024x1024 for most modern models). Connect it to the KSampler's latent image input. This is the blank slate the AI starts refining.

The KSampler outputs latent data (pink). You need to convert it to a visible image.

Add a VAE Decode node. Connect the KSampler's output to it. Load a VAE model and connect that too. Then add a Save Image node and connect the VAE Decode output (blue) to it.

Click run. Your image appears.









The core generation node. It takes noise and gradually refines it into an image that matches your prompt, using the model, positive/negative conditioning, and a starting canvas.

Check your diffusion model's documentation. Each model specifies compatible CLIP and VAE files. Mismatched models produce poor results or errors.

Yes. All Floyo workflows are live sharable links that can be sent to other creators directly. To share with the same settings or inputs, add folks to your private Floyo team and share with them. You can also download the workflow JSON from the canvas.

Not with text alone. For structural control like pose, depth, or edges, you need ControlNet. That's covered in Ep 10: ControlNet basicsEpisode 10

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