ComfyUI Workflow Build Text2Img + Latent Upscale + Model Upscale | ComfyUI Basics | Stable Diffusion

This paragraph introduces the tutorial's objective: constructing a fundamental text-to-image workflow from scratch using Kyui. It also mentions the comparison with Stable Diffusion's automatic 1.1 to provide insights into creating a workflow. The process begins with clearing the workspace and accessing the checkpoint node, which is fundamental for the workflow. Two methods for adding nodes are discussed: right-clicking for the 'Add node' section or double-clicking to search for specific nodes like the 'checkpoint'. The paragraph concludes with a comparison of these methods and a transition to the next topic, which is the automatic 1.1's positive and negative prompt sections.

The second paragraph delves into the creation of prompt nodes for both positive and negative prompts, which are essential for guiding the image generation process in Kyui. It outlines three methods to add these nodes: using the 'Add node' option after a right-click, double-clicking to search, or dragging from the 'load checkpoint' node. The paragraph also covers the renaming of these nodes for clarity. Moving on, it discusses the image generation settings found in automatic 1.1, such as sampling method, scheduler types, steps, width, height, CFG scale, seeds, and emphasizes their importance. The speaker then guides the audience back to Kyui to add a 'K sampler' node for image generation, explaining different ways to access and connect it with the previously created prompt nodes.

This paragraph focuses on the final steps of the basic text-to-image workflow, including the addition of an 'empty latent image' node to define width and height, and the connection of a 'VAE decode' node to convert information into an image. It describes the process of generating an image using specific settings and prompts, and then compares the results with different resolutions. The paragraph also introduces the integration of LoRA into the workflow to enhance image generation, explaining how to add and connect the 'load LoRA' node, and the importance of connecting its model and clip points to the existing workflow. The results of adding LoRA are demonstrated, showcasing the improved image detail and style.

The fourth paragraph discusses the process of adding latent upscale functionality to the workflow. It explains how to incorporate a 'latent upscale by' node, connect it with the initial image output, and set up a new 'K sampler' for the upscaled image. The paragraph also covers the connection of model, positive prompt, and negative prompt nodes to the upscale process. The importance of adjusting the 'denoising strength' for better results is highlighted, and the speaker demonstrates the effect of this setting on the image output. The paragraph concludes with a comparison between the original image and the latent upscaled image, emphasizing the changes and potential disturbances caused by the denoising strength.

In the final paragraph, the speaker wraps up the tutorial by adding a 'model upscale' node to the workflow, which is used to further enhance the resolution of the generated images. The process involves connecting the output of the latent upscale to the 'upscale by model' node and adjusting settings like the scale factor. The results of the model upscale are compared with the latent upscale and the original text-to-image output, showcasing the differences in resolution and image quality. The paragraph concludes with a cleanup of the workflow, organizing the nodes into groups for clarity, and reflecting on the completed workflow, which includes a simple text-to-image workflow, LoRA integration, latent upscale, and model upscale.