Welcome to text-to-image prompt techniques.

After watching this video,

you'll be able to explain
common image prompting techniques
used to improve the quality and impact of images,
and apply these techniques to write
better prompts for image generation.
Images are an essential part of communication
and are used in various fields such as marketing,
advertising, education, journalism, and many others.
Nonetheless, certain images excel in
their ability to convey emotions
more effectively than others.
An image prompt is a text description
of an image that you want to generate.
It can be as simple as a single word or phrase,
or it can be more detailed describing the composition,
colors, and mood of the image.
To increase the impact of images obtained through
generative AI models and
make them more convincing and compelling,
you can use image prompting techniques.
These techniques aim to improve the quality, diversity,
and relevance of images produced by generative AI models.
There are different image prompting techniques that
can be used to improve the impact of images.
Let's learn about these techniques one by one.
Style modifiers are descriptors used to influence
the artistic style or
visual attributes of images
produced by generative AI models.
These descriptors can help
the model produce graphics with
innovative style while conforming to
the structure and content of the input prompt.
You can modify the various visual elements
of an image like color, contrast,
texture, shape, and size,
and generate output that is
aesthetically appealing and visually pleasing.
Your prompt can include information about
miscellaneous art styles, historical art periods,
photography techniques, types of art materials used,
and even traits of well-known brands
or artists you want the model to emulate.
All this information can help the generative model
understand the desired appearance
or style of the output image.
Here are a few examples of
style modifiers used in image prompts.
The style modifiers used
in these prompts have been highlighted.
Moving on to the next image prompting technique,
that is quality boosters.
High-quality images are more
convincing and reliable as compared to low-quality ones.
Images with low resolution
frequently exhibit blurriness and pixelation,
making it difficult for viewers to
discern the finer details within the image.
On the other hand, images with high-resolution
guarantee essential visibility and readability.
The perceived worth of an image can be
raised by using high-quality graphic design.
Quality boosters are terms
used in an image prompt to enhance
the visual appeal and improve
the overall fidelity and sharpness of the output.
These are specific terms that can direct
the generative AI model to
perform steps like noise reduction,
sharpening, color correction, and resolution enhancement.
You can use terms like high resolution,
2k, 4k, hyper-detailed,
sharp focus, complimentary colors,
and many others in your image prompts
as quality boosters.
They can enhance specific features of the image,
resulting in more coherent output.
Let's look at some examples to understand how
quality boosters can be used in image prompts.
Terms such as highlights the texture,
4k resolution, sharp, crisp details,
and fine lines, complementary colors,
blurred background, and stand out
are quality boosters used in the given image prompts.
The third image prompting technique is repetition.
This technique leverages the power of iterative sampling
to enhance the diversity of
images generated by the model.
Repetition involves emphasizing
a particular visual element
within an image to create
a sense of familiarity for the model,
allowing it to focus on
a specific idea or concept you want to highlight.
This can be accomplished by repeating
the same word or similar phrase within the image prompt.
Repetition helps reinforce the message conveyed
through the image and increase
the memorability of the model.
Rather than producing just one image based on a prompt,
the model generates multiple
images with subtle differences,
resulting in a diverse set of potential outputs.
This technique is particularly
valuable when generative models are confronted with
abstract or ambiguous prompts to
which numerous valid interpretations are possible.
Let's look at some examples of
repetitive words used in an image prompt.
Words such as tiny,
dense, enormous, vast, serene,
clear, and lush have been repeated
many times to focus on a specific idea.
The fourth image prompting technique is weighted terms.
Weighted terms refer to the use of words or phrases that
can have a powerful emotional or psychological impact.
For example, words such as free,
limited time offer, and guaranteed,
are often used in advertising to
elicit a sense of urgency, security, and trust.
Similarly, words such as luxury, premium,
and exclusive are used to create
a sense of exclusivity and sophistication.
Generative AI models allow you to
give positive or negative weights to such terms,
to emphasize or de-emphasize a certain emotion.
Using weighted terms in an image prompt can
help create images that are memorable and convincing,
and can draw emotional responses from the audience.
Here are some examples of
weighted terms used in an image prompt.
As you can see in the first example,
a weight of positive ten is given to the word warm,
whereas the weight of crackling is positive eight.
This means the generative model must focus more
on the word warm and a little less on the word crackling.
Similarly, in the second example,
a positive six weight is given to the word shimmering,
and the weight of neon-lit is positive eight,
so the model should focus more on neon-lit.
Whereas in the last example,
negative weight of six is given to the word colorful,
and a positive weight of ten is given to exotic.
This means the model must emphasize the word
exotic and de-emphasize the word colorful.
The fifth image prompting technique
is fix deformed generations.
This technique is used to modify any deformities
or anomalies that may impact
the effectiveness of the image.
Deformities in an image can
include conditions like distortion,
particularly on human body parts like hands or feet,
pixelation or other image quality issues that
can detract from the visual appeal
and clarity of the image.
This can be mitigated to
some extent by using good negative prompts.
Here are some examples of
deformed generation prompting
techniques used in image prompts.
You can see that in all these examples,
good negative words have been used to
mitigate the issues of deformed images.
In this video, you learned that
image prompting techniques play a vital role
in advancing the image generation capabilities
of generative AI models.
Style modifiers, quality boosters,
repetition, weighted terms,
and fix deformed generations are
five techniques that can be used to
improve the impact of images produced.
By incorporating these techniques,
one can create more memorable, engaging,
and persuasive visuals that can
effectively communicate the intended message.