Overview
The Load Checkpoint node supplies three important outputs used in the workflow:
Model: The image-generation model used by the sampler.
CLIP: The component used by the text-encoding nodes to convert positive and negative prompt text into conditioning.
VAE: The component used to convert between a latent representation and a visible image.
Two CLIP Text Encode nodes are connected to the checkpoint's CLIP output. One holds the positive prompt: the visual content that should appear. The other holds the negative prompt: the content or qualities that should be avoided. Renaming them Positive Prompt and Negative Prompt makes their roles explicit.
The KSampler receives the model, positive conditioning, negative conditioning, and a latent image. It is the algorithmic stage that transforms the starting latent representation according to the prompts and sampling settings.
For text-to-image generation, the input is an Empty Latent Image. Despite the name, it is not a blank visible canvas. It represents random noise. The generation process begins with that noise and removes portions of it over a sequence of steps until the latent representation corresponds to the requested image.
The KSampler output is still latent data and cannot be viewed directly. A VAE Decode node converts it into an image. The VAE input normally comes from the checkpoint because most checkpoints in this workflow include a VAE. The decoded image is then connected to either Preview Image or Save Image.
Preview Image displays the output without automatically writing every generation to disk. A previewed image can still be saved manually by right-clicking it and selecting Save Image. Save Image both displays and automatically saves the result.
Nodes can be added in several ways. Right-clicking the canvas exposes the complete node menu, but the menu becomes difficult to navigate because of the number of available nodes. Double-clicking the canvas and searching by node name is faster. Dragging outward from an existing connector is also useful because ComfyUI proposes nodes that accept the relevant data type. Dragging from the checkpoint's CLIP connector, for example, proposes a CLIP Text Encode node.