Creating images using neural networks is becoming increasingly popular, and everyone who uses such tools faces the question: how to get the right image? One of the key factors affecting the result is composition – the arrangement and interaction of elements in the image. In this article, we’ll look at how to guide the neural network with the right composition in Promtas and achieve the desired visual effect.
Understanding composition in images
Composition is the art of arranging the elements of an image so that they work harmoniously together. In visual art, composition includes elements such as balance, perspective, proportion, symmetry and focus. For generating images using neural networks, it is important to formulate queries correctly to get the desired distribution of elements.
Use of guiding elements
To create balanced and harmonious images, it is important to use guiding elements. For example, if you want to create a landscape with a lake, specify where it should be located: “lake in the foreground, mountains in the background”. Specifying the location of objects helps the neural network to organize the elements of the image correctly, avoiding chaos or incorrect composition.
Example:
- “mountains in the background, lake in the foreground, trees on the sides, and the sky is clear with a few clouds” – such a query will create a clear structure.
The importance of symmetry and asymmetry
Symmetry in a composition can add harmony and calmness to an image, while asymmetry adds dynamism and interest. It is important to specify what type of composition you want.
Example of symmetry:
- “A central composition with a main object in the middle (such as a statue in a park) and a balanced background.”
Example of asymmetry:
- “Asymmetrical image: a flowering bush on the side and an empty field in the center with a lonely road leading into the horizon.”
Using perspective and depth
Perspective and depth add realism to images. Queries can specify how much of the perspective effect should be expressed, such as “deep perspective”, “image from a low vantage point”, or “high perspective from an overhead view”.
Example:
- “A cityscape with perspective from the main street going off into the distance, buildings gradually getting smaller”.
Creating focus through composition
To create focus on a particular object, specify what should be the main focus in the image. For example, if you want the main element to be a sculpture or a person, specify where the focus should be.
Example:
- “The person is standing in the center of the scene, the other elements are slightly smeared to focus attention on the main object.”
Balance and distribution of light and shadow
Lighting also plays an important role in composition. The use of light and shadow can create contrast, highlight objects, and establish atmosphere. Specify what type of lighting or shadows you want in the image.
Example:
- “Silhouette of a man against a setting sun, with long shadows from trees on the ground.”
Testing and Adapting
Not always the first request will lead to a perfect result. It is important to test different variations of the composition, to refine the placement of objects or to change accents. Sometimes small changes, such as adding a “light background” or “a slight skew to the right side” can lead to significant improvements in the composition.
Conclusion
In order to successfully generate images using neural networks, it is important to be able to guide the system through the correct composition. The use of guiding elements, symmetry or asymmetry, focus and lighting in queries all help neural networks create more harmonious and higher quality images. Apply these principles to your promts, test different variations and adapt queries depending on the result to achieve the best visual effect.
