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

The video tutorial begins with an introduction to creating a basic text-to-image workflow from scratch using Kyui. The instructor discusses the importance of obtaining a checkpoint node and demonstrates two methods for adding nodes to the workflow: right-clicking to access the 'add node' section or double-clicking to use a search bar. The tutorial also compares the workflow with Stable Diffusion's automatic LL, emphasizing the need for positive and negative prompt sections. These prompts are added through various methods, including dragging from the load checkpoint node or using the search bar. The instructor then connects the prompts to the load checkpoint node and renames them accordingly, setting the foundation for the text-to-image generation process.

This paragraph delves into enhancing the basic text-to-image workflow with features like the addition of a 'K sampler' node, which is crucial for image generation. The instructor explains how to add this node and connect it to the positive and negative prompts. The tutorial also covers the importance of setting the width and height for the image using an 'empty latent image' node. The workflow is then tested with specific parameters, and the results are shared, demonstrating the initial output of a stylized image. The video continues with the integration of 'Lora' nodes to further refine the image generation process, showing how to connect these nodes and the impact they have on the final image. The instructor also demonstrates how to duplicate Lora nodes for more complex workflows.

The script explains how to incorporate latent upscale and model upscale techniques into the existing workflow. The 'latent upscale by' node is introduced, which requires a sample from the initial workflow's output. The instructor guides the audience through connecting the necessary nodes for upscaling, including a 'K sampler' for the upscaled latent image and the appropriate prompts. The workflow is then simplified using a 'reroot' function to organize and streamline the process. The tutorial also covers adjusting the denoisng strength to balance changes in the upscaled image, comparing the original and upscaled results, and the importance of selecting the right scale factor for the desired outcome.

The final part of the tutorial focuses on integrating the 'upscale by model' feature into the workflow. The instructor adds a node for this purpose and connects it to the previously generated latent upscale image. The settings for the upscale model are adjusted, and the results are compared to the original text-to-image output, highlighting the differences in resolution and quality. The workflow is then cleaned up for clarity, and the instructor guides the audience through the process of simplifying the workflow and organizing it into groups. The video concludes with a summary of the workflow's components, including the basic text-to-image, Lora nodes, latent upscale, and model upscale features.

In the conclusion of the video, the instructor recaps the entire process of building a comprehensive text-to-image workflow from scratch. The workflow includes a simple text-to-image setup, the addition of Lora nodes, latent upscale techniques, and model upscale methods. The audience is encouraged to provide feedback and suggestions for future video content in the comments section. The instructor expresses gratitude for watching and signs off with a warm farewell, indicating the end of the tutorial.