HUMAN CREATIVITY + MACHINE INTELLIGENCE
HUMAN CREATIVITY + MACHINE INTELLIGENCE
How it works
How it works
Not sure exactly how AI generates such amazing images. Your in Luck! In the space below you will be provided with some insight into how it all works.
Not sure exactly how AI generates such amazing images. Your in Luck! In the space below you will be provided with some insight into how it all works.
THE PROCESS
THE PROCESS
THE PROCESS
THE PROCESS
THE PROCESS
THE PROCESS
Stepping into the world of AI-generated photography, each image is the result of cutting-edge artificial intelligence using original prompts. The process of generating images using AI, as seen in programs like Midjourney, Leonardo.ai, and Adobe Firefly, typically involves a technique called Generative Adversarial Networks (GANs) or some variation of it. Here's a high-level explanation of how it works:
Stepping into the world of AI-generated photography, each image is the result of cutting-edge artificial intelligence using original prompts. The process of generating images using AI, as seen in programs like Midjourney, Leonardo.ai, and Adobe Firefly, typically involves a technique called Generative Adversarial Networks (GANs) or some variation of it. Here's a high-level explanation of how it works:
Generative Adversarial Networks (GANs): GANs consist of two neural networks, the Generator and the Discriminator, which are trained simultaneously in a competitive manner.
Generative Adversarial Networks (GANs): GANs consist of two neural networks, the Generator and the Discriminator, which are trained simultaneously in a competitive manner.
Generator Network: This network takes random noise as input and tries to generate realistic-looking images. Initially, its output is random and meaningless.
Generator Network: This network takes random noise as input and tries to generate realistic-looking images. Initially, its output is random and meaningless.
Discriminator Network: This network's job is to distinguish between real images (taken from a dataset) and fake images generated by the Generator. It is trained to classify images as either real or fake.
Discriminator Network: This network's job is to distinguish between real images (taken from a dataset) and fake images generated by the Generator. It is trained to classify images as either real or fake.
Training Process: During training, the Generator aims to produce images that are indistinguishable from real ones to fool the Discriminator, while the Discriminator tries to become better at distinguishing real from fake. As training progresses, both networks improve.
Training Process: During training, the Generator aims to produce images that are indistinguishable from real ones to fool the Discriminator, while the Discriminator tries to become better at distinguishing real from fake. As training progresses, both networks improve.
Adversarial Process: The Generator and Discriminator are in a constant feedback loop. The Generator gets feedback from the Discriminator on how well its generated images resemble real ones, and it adjusts its parameters accordingly to generate more realistic images. Meanwhile, the Discriminator gets better at distinguishing real from fake images.
Adversarial Process: The Generator and Discriminator are in a constant feedback loop. The Generator gets feedback from the Discriminator on how well its generated images resemble real ones, and it adjusts its parameters accordingly to generate more realistic images. Meanwhile, the Discriminator gets better at distinguishing real from fake images.
Convergence: Ideally, this process continues until the Generator produces images that are so realistic that the Discriminator can't tell them apart from real images. At this point, the training is considered to have converged.
Convergence: Ideally, this process continues until the Generator produces images that are so realistic that the Discriminator can't tell them apart from real images. At this point, the training is considered to have converged.
Output: Once trained, the Generator can produce new images by taking random noise as input and generating images based on the patterns it learned during training.
Output: Once trained, the Generator can produce new images by taking random noise as input and generating images based on the patterns it learned during training.
LET'S LOOK AT AN EXAMPLE
LET'S LOOK AT AN EXAMPLE
LET'S LOOK AT AN EXAMPLE
LET'S LOOK AT AN EXAMPLE
LET'S LOOK AT AN EXAMPLE
LET'S LOOK AT AN EXAMPLE
Let's look at a sample prompt and the corresponding images that were produced by Microsoft's AI image generator
CAN CREATIVES CREATE WITH AI?
CAN CREATIVES CREATE WITH AI?
CAN CREATIVES CREATE WITH AI?
CAN CREATIVES CREATE WITH AI?
CAN CREATIVES CREATE WITH AI?
CAN CREATIVES CREATE WITH AI?
The quick answer is YES. Designers can work with AI and still be creative. Taking the above example; while the first 2 images are generated with AI, taking the two separate images and combining them to make a more compelling visual element is where the creative shines through in the final image.
The quick answer is YES. Designers can work with AI and still be creative. Taking the above example; while the first 2 images are generated with AI, taking the two separate images and combining them to make a more compelling visual element is where the creative shines through in the final image.
©2025 by Mind2Canvas. All rights reserved.
© 2025 by Mind2Canvas. All rights reserved.
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