【Stable Diffusion】图生图 超详细使用方法和参数设置诀窍

The speaker, AI Xiao Wangzi, introduces the topic of image-to-image techniques using Stable Diffusion. They recommend viewers to like and save the video for future reference, as it promises to be the most comprehensive guide available. The speaker clarifies the functions of various features like partial redraw, graffiti redraw, local painting, and batch processing within the tool. They begin by navigating through the web interface, highlighting the differences between text-to-image and image-to-image processes, emphasizing the importance of having an initial image to transform into a desired style. The speaker also introduces a plugin called 'all-in-one-prompt' and provides its installation link. They explain the interface, focusing on the unique aspects of image-to-image, such as the image placement box and the涂鸦绘制 feature. The concept of denoising strength, which dictates the level of transformation from the original image, is introduced with examples showing how it affects the final output.

The speaker demonstrates how to adjust the background and maintain the character's likeness by manipulating the CFG scale and redraw strength. They showcase the transformation of an image into a beach background while keeping the character's features largely unchanged. The video then delves into the sketching feature, explaining the similarities and differences between sketching and image-to-image processes. The speaker guides viewers on how to use the sketching tools, including the color palette, brush size, and eraser. They also highlight the utility of the canvas zoom plugin for detailed work and provide a link for its installation. The power of sketching is exemplified by turning simple doodles into fully rendered images, showcasing the potential for creating scenes from basic drawings.

The speaker explains the concept of local redraw, which is crucial for tasks like AI face swapping. They discuss the parameters involved in local redraw, such as scale mode and mask blur, which adds a Gaussian blur effect to the mask. The video illustrates how mask blur can soften the edges of the redraw area for a more natural transition. The speaker also differentiates between mask modes, which determine whether the redraw affects the masked area or the unmasked area. They discuss the 'fill original potential noise' and 'no potential noise' options, explaining how they influence the rendering process by either disregarding or considering the original image elements. The video also touches on the differences between 'latent noise' and 'latent nothing' modes, which affect the level of detail and randomness in the output image.

The speaker continues the tutorial by discussing advanced masking techniques, including the use of 'inpaint sketch' and 'inpaint upload' for localized image manipulation. They explain the concept of 'reference radius' in local redraws and its impact on how closely the output adheres to the original image. The video clarifies the differences between sketching, local painting, and local painting with a graffiti mask, emphasizing when and how to use each technique. The speaker also covers the use of mask transparency and its effect on the rendering process. They provide a practical demonstration of using Photoshop to create a mask for uploading into the image-to-image tool for precise control over image manipulation, particularly useful for tasks like model dressing or face swapping.

The final part of the tutorial covers batch processing, where the speaker instructs viewers on how to set up folders for input images, output directories, and local redraw masks, emphasizing the need for English naming to avoid errors. They explain the process of ensuring that image and mask names match for successful batch processing. The video concludes with a discussion on CLIP inversion, a feature that can deduce keywords from an image when the original prompt is unknown. The speaker differentiates between two types of CLIP inversion and mentions a plugin called 'Trigger' for more accurate results. They wrap up the tutorial by encouraging viewers to like and subscribe for more AI-related content and tutorials.