SeaArt AI ControlNet: All 14 ControlNet Tools Explained

This paragraph introduces the viewer to the 14 CR AI Control Net tools, which are designed to provide more predictable results in image generation. It explains how to access these tools through the 'Control Net' feature in the application and emphasizes the importance of selecting the appropriate source image. The paragraph outlines the first four options, which include Edge Detection algorithms and their respective control net models: Canny, Line Art, Anime, and HED. Each model is briefly described, highlighting their unique capabilities in altering images, such as color and lighting adjustments. The speaker demonstrates the differences between these models by adding a source image and discussing the autogenerated image description, which can be edited as a prompt. The paragraph further explains the various settings and options within the control net, such as the pre-processor, control net mode, control weight, and common image generation settings. The speaker provides examples of how these settings impact the final result, comparing the original and generated images for each control net option. The discussion includes the strengths and weaknesses of each model, such as the soft edges produced by the Canny model, the high contrast and digital art appearance of the Line Art model, the low overall image quality of the Anime model, and the significant issues absent in the HED model. The paragraph concludes with a demonstration of using 2D anime images and the effectiveness of the control net models in preserving the main shapes of architectural subjects.

The second paragraph delves into the advanced features and tools available in the CR AI Control Net, focusing on pre-processors and their applications. It begins by discussing the Scribble HED model, which creates a simple sketch based on the input image, and how the generated images may not replicate all the features and details from the original. The paragraph then introduces the Pose detection feature, which captures the pose of a person from the image and reflects it in the generated images. The speaker also explains the Normal Bay and segmentation features, which create a depth map and divide the image into different regions, respectively. The Color Grid tool is highlighted for its ability to extract color palettes from the image and apply them to generated images, although it is noted that it may not always be 100% accurate. The paragraph further discusses the Shuffle forms and warps feature, which restructures different parts of the image and creates new images based on the description while maintaining the same colors and overall atmosphere. The reference generation tool is introduced as a unique option for creating similar images based on the input image, with the style Fidelity value controlling the influence of the original image on the generated one. The paragraph concludes with an example of using the image-to-image option to create more detailed variations of the image and the ability to use up to three control net pre-processors simultaneously. The speaker demonstrates this by using a cityscape image with the color grid pre-processor and adding the Line Art pre-processor to generate an image with combined details and colors. Lastly, the paragraph introduces the preview tool, which allows users to get a preview image from the input for control net pre-processors, with the processing accuracy value affecting the quality of the preview image. The preview image can be further manipulated using an image editor for greater control over the final result.