What Does Seed Mean in AI Art | Everything You Need to Know
When it comes to AI-generated art, the term “seed” is often talked about as a foundational component for unique, controlled results. As AI tools become more sophisticated, understanding the concept of seeds becomes crucial for businesses seeking to leverage AI-driven art for character generation.
When asking what seed means in AI Art, you might revert to the conventional definition of the word. In reality, you aren’t far off. Keep reading to understand more about this unique feature of AI image generation.
Read next: Learn more about Shai’s AI Character Generator.
What is a seed in AI art?
In AI art, a seed is a numeric or image-based starting point that shapes how an AI model generates an output. Think of the seed as a guiding code that directs the AI’s generative process, setting outlines for style, structure, and even emotional tone. Each seed number or seed image provides a unique identifier, which an AI model uses to construct visual content.
When a specific seed is used, the AI model will draw upon that seed to produce a consistent output each time, as long as the other variables (such as model type and prompts) are the same. This means that you can control the ability of your AI-generated art to be duplicated by specifying or documenting the seed values you use.
Next Read: Discover the power of Shai’s AI Storyboard Image Generator.
The difference between seed numbers and seed images
There are two primary types of seeds used in AI art: seed numbers and seed images. Both have distinct roles in generating images, and understanding their differences can help creatives make informed decisions about which approach to use.
Seed numbers
Seed numbers are the more common and straightforward form of seeding. They consist of numeric values that serve as the backbone of the AI’s creative process. Each seed number corresponds to a specific configuration of values within the model’s algorithms. These values guide the AI in arranging pixels, colors, shapes, and patterns in a consistent, reproducible way.
For example, if you use the seed number “1234” to generate a character design, the same seed, combined with the same prompts, will reproduce the exact same character design every time. By adjusting the seed number, you can make small or significant changes to an existing design without losing its core attributes.
Seed images
Seed images, by contrast, act as visual references that guide the AI more interpretatively. Instead of numeric input, the AI is provided with an actual image as the inspiration for generating a new output. Seed images are particularly valuable when the goal is to generate an output that captures the essence or style of a particular image without duplicating it.
For instance, a producer might use an existing product design, mascot, or cast member’s photo as a seed image. The AI can then create new variations or complementary designs that align with the aesthetic or thematic elements of that initial image. This approach is useful when an advertiser wants to maintain a consistent visual identity or a production crew want to realise a storyboard.
How do seeds work in AI art generation?
Understanding the mechanics of how seeding works can highlight why this process is so integral to AI-driven creativity, especially for character design.
1. Pseudo-random number generation (PRNG)
When a seed number is entered, the AI model’s algorithm generates a sequence that influences the entire image-creation process. This sequence dictates pixel arrangements, color distributions, and other aesthetic choices. Using the same seed number will always produce the same sequence, allowing for repeatability.
2. Interpreting Seed images
For seed images, the AI model first interprets the image’s visual elements — such as shape, color, and texture — and then uses these elements as a framework for creating new designs.
3. Application of generative models
In character generation, popular models like GANs (Generative Adversarial Networks) and diffusion models use seeds to establish an initial point for image creation. The seed acts as an anchor, guiding the model through its learned patterns.
4. Continuous adjustment
Designers can modify a seed or seed image by incrementally changing the number or image source, producing diverse outputs while preserving the theme. In practice, this approach helps to explore a range of character designs efficiently.
Prompt used in Shai: Kayla enters the frame, saying hello to Matt and the viewers.
Why people use seeds in AI art
For companies utilizing AI-driven character generation, seeds offer numerous advantages that streamline the creative process, enhance consistency, and improve resource management.
1. Control and consistency
Using specific seeds enables predictable results, making it easier to recreate designs that align with a brand’s visual identity.
2. Efficient iteration
Seeds enable faster and more cost-effective iteration in character design. The use of seeds speeds up the creative process and minimizes the need for additional design resources.
3. Experimentation and creative flexibility
Seeding allows for structured experimentation, giving businesses the flexibility to explore creative directions while staying within established visual parameters.
4. Enhanced collaboration across teams
Seed values act as a common language, facilitating easier communication and alignment among stakeholders, even when working across different projects or departments.
5. Cost savings through reusability
By saving and reusing seeds, businesses can lower production costs associated with custom character creation.
Example of AI image generation with Shai
Why Shai doesn’t use seeds in AI art
Here are some reasons not to use seeds in AI art:
- Lack of True Uniqueness: Relying on a specific seed can limit the randomness and spontaneity that AI art can offer. This makes achieving completely unique, fresh outputs harder.
- Risk of Over-Replication: Reusing the same seed can lead to outputs that appear overly similar, which may reduce creative diversity.
- Limited Creative Exploration: Seeds provide control, but this control can be restrictive if it limits experimentation.
- Inflexibility for Dynamic Applications: For projects requiring designs that evolve (such as interactive art or generative storytelling), seeds may limit flexibility and prevent the AI from adjusting dynamically to context or user interaction.
- Time Investment in Documentation: Using seeds effectively requires documentation, tracking, and organization to ensure consistency, which can add to project management tasks and complexity.
Tip: Learn how to storyboard with this in-depth guide.
Wrapping up seeds in AI art
As you can see, seeds offer a tool to control, reproduce, and experiment with image designs and processes and enhance consistency. While seeds are great for generalized AI image production, they do have their limitations.
With that in mind, Shai avoids seeds in their image production to ensure a dynamic image production process that results in unique and creative imagery that’s responsive to your script. Whether generating characters or fully visualizing your pre-production processes, Shai can help you with it all.