ComfyUI: Yolo World, Inpainting, Outpainting (Workflow Tutorial)

The speaker, Mali, introduces a comprehensive tutorial on advanced image segmentation, inpainting, and outpainting techniques using a zero-shot instance segmentation tool. They demonstrate the tool's effectiveness on low-resolution images and AI-generated images, adjusting colors in post-processing. The tutorial covers seven workflows, requiring basic knowledge of Comfy UI. Mali thanks paid members and explains the installation process for various nodes, including YOLO World for real-time object detection and segmentation, and Comfy inpainting nodes using the Focus inpainting model. The tutorial also discusses using differential diffusion and self-attention guidance for high-quality image processing.

Mali details the process of using YOLO World for object detection and segmentation. They explain the use of different model sizes, with a preference for the large model due to its accuracy. The tutorial shows how to connect the YOLO World node with an image input, preview, and mask output. Mali also discusses the use of the WD14 tagger node to list tags in an image, which aids in refining object detection. They explore the impact of confidence and IOU thresholds on detection accuracy and demonstrate handling low-resolution and poor-quality images with the YOLO model.

The speaker describes how to apply color or gradients to images and blend them using image blending nodes. They also discuss color correction using specific nodes and the process of segmenting individual elements from an image using YOLO World nodes. Mali demonstrates the process of refining masks and cutting elements from the source image, as well as pasting them onto a new image. They highlight the importance of using the correct prompts and thresholds when working with YOLO World for accurate segmentation.

Mali continues the tutorial by demonstrating how to segment clothing and background elements from images using specific keywords with the YOLO World node. They show how to refine the segmentation process by adjusting prompts and using bitwise operations to subtract masks. The tutorial also covers techniques for cleaning up mask artifacts and cutting masked parts from the source image. Mali emphasizes the importance of using the correct keywords and settings for accurate segmentation.

The focus shifts to techniques for removing subjects or objects from images. Mali introduces two inpainting models: MAT and Llama, discussing their performance based on the image data set. They demonstrate the process of removing a subject using the selected mask and inpainting models, highlighting the differences in performance between MAT and Llama. The tutorial also covers the use of grow mask nodes, positive prompts, and multiple passes for refining the inpainting process.

Mali shows advanced techniques for refining image details and outpainting. They discuss the use of a VAE encode and condition node, along with a focus patch, for replacing subjects or objects within an image. The tutorial covers the process of adding a preview bridge for manual masking, using a blur mask node to preserve colors, and connecting various nodes for outpainting. Mali also demonstrates how to use different settings and prompts for outpainting in different directions.

The final part of the tutorial covers outpainting techniques, where Mali demonstrates how to extend image edges with new content using various nodes and settings. They show examples of outpainting horizontally and vertically, using different mask area styles and prompts for more accurate results. Mali concludes the tutorial by summarizing the key points and expressing hope that viewers have learned valuable new skills in Comfy UI.