{"id":22663,"date":"2024-11-06T09:41:26","date_gmt":"2024-11-06T01:41:26","guid":{"rendered":"https:\/\/www.1ai.net\/?p=22663"},"modified":"2024-11-06T09:41:26","modified_gmt":"2024-11-06T01:41:26","slug":"comfyui%e5%b7%a5%e4%bd%9c%e6%b5%81%ef%bc%8c%e4%b8%8d%e7%94%a8%e5%86%99%e6%8f%90%e7%a4%ba%e8%af%8d%e7%9a%84%e4%ba%ba%e5%83%8f%e6%8f%92%e4%bb%b6%e7%94%9f%e6%88%90%e4%bb%8e%e5%87%ba%e7%94%9f%e5%88%b0","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/22663.html","title":{"rendered":"comfyui workflow, no need to write prompt words for portrait plugin to generate photos of faces from birth to 100 years old"},"content":{"rendered":"

Today's article topic introduces one of the latestAge Transformation<\/strong>Framing:SAM<\/strong>. This is a process framework that can demonstrate the process of changing the look and appearance of an individual character over time as the character transforms with age. The framework encodes real facial images directly into the latent space of a pre-trained unconditional GAN (e.g., StyleGAN), controlled by a given age offset. It also uses a pre-trainedAge Regression Network<\/strong>, explicitly instructing the encoder to generate potential codes corresponding to the desired age.SAM<\/strong>methods treat successive age processes as regression tasks between the input age and the desired target age, providing fine-grained control over the generated images. Furthermore, unlike other methods that operate only in potential space and use a priori control of the age paths, theSAM<\/strong>method more decoupled and nonlinear paths. The end-to-end nature of the method, coupled with the rich semantic potential space of StyleGAN, allows for further editing of the generated images.<\/p>\n