Prompt: Detailed oil painting. Parts of painting is photorealistic, other parts are abstract. A beautiful young witch in a flowing white vintage dress with a floral hat sitting on a rock by a lakeside. Psychedelic details. Rolling lush green hills in the background with colorful wildflowers in the foreground. A majestic castle on a distant hill under a sky with soft clouds during the golden hour.
Prompt: Photoreal,Fantasy,Fairy Tale, Miranda Elegant Beautiful Woman with a smile,Steampunk Brown Leather and Lace Clothing,Arms Outstretched Left and Right,Clear Distinct Realistic Eyes,,Two Goblin Women Smiling and Holding Large Mugs of Beer Standing Either side of Miranda,Dark Dense Oak Tree Woodland,Trees With Scary Faces, 'mdjrny V5 style'
Testing AIVision model's reaction to non-prompt-like information.
Model:
AIVision
Size:
1024 X 1024
(1.05 MP)
Used settings:
Prompt: -Analog nature of neural computations: Although neural networks are implemented on digital computers, the computations they perform are often analog in nature. For instance, the activation functions used in neural networks, such as sigmoid or ReLU, can take on a continuous range of values, which is more akin to analog computations. This allows neural networks to model complex, continuous relationships between inputs and outputs.
-Emergent properties: Neural networks are designed to learn and represent complex patterns and relationships in data. Through the process of training, the network's weights and biases are adjusted to capture these patterns. The emergent properties of neural networks, such as their ability to recognize images, understand language, or make predictions, arise from the collective behavior of many simple, interconnected processing units. This is similar to how complex systems in nature, like ant colonies or the human brain, exhibit emergent properties that transcend the capabilities of individual components.
-High-dimensional feature spaces: Neural networks, especially deep ones, can learn to represent data in high-dimensional feature spaces. These spaces can capture subtle and intricate relationships between input features, which might not be apparent in the original, lower-dimensional data. The ability to learn and operate in these high-dimensional spaces is a key factor in the success of neural networks in various applications.
-Adaptive and flexible: Neural networks can adapt to new data and tasks through continuous learning. This adaptability allows them to generalize and transfer knowledge across different domains, further transcending their binary foundation.
Dream Level: is increased each time when you "Go Deeper" into the dream. Each new level is harder to achieve and
takes more iterations than the one before.
Rare Deep Dream: is any dream which went deeper than level 6.
Deep Dream
You cannot go deeper into someone else's dream. You must create your own.
Deep Dream
Currently going deeper is available only for Deep Dreams.