Image created in Leonardo.ai and added below in Midjourney

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The neural network may refuse to work with the request, considering it inappropriate. However, most often the result is successful, and the neural network turns a close-up of a person into a general one better than its competitors, drawing the figure in full growth.


Image created in Leonardo.ai and added bottom and side details in Midjourney


Working with ready-made images is a natural step in the development of generative neural networks. Generating images from scratch is easier than refining ready-made ones, since in this case the model creates an image from noise, gradually forming a whole picture.

To edit an existing image, AI must first understand what has already been drawn, build a physical model of the scene, including the interaction of objects, and then harmoniously fit in the new belgium phone number lead elements, preserving the style and color palette. This is significantly more difficult, as it requires a comprehensive approach to composition and precision in working with the image.



Why New Tools Are Bringing Content Labeling Back into the Spotlight
The technological advances of Midjourney and other generative networks have once again reignited the debate about the need to label AI-generated content.

Marking can protect social media users from fakes. But neural networks do not yet have the technical ability to add hidden watermarks to images without degrading the quality of generation.

The marking is supposed to signal the AI ​​origin of the file, but the Midjourney developers have not yet implemented this feature.

In the process of generating an image, the neural network takes a random noise field, like the analog interference on an old TV screen, and gradually refines it, obtaining contours and superimposed filters. The model repeats this sequence until the desired result is obtained.