# The Prompt Artists

Minsuk Chang  Google, Inc.Seattle, WashingtonUnited States
<EMAIL_ADDRESS>,  Stefania Druga  Google, Inc.Mountain View,
CaliforniaUnited States<EMAIL_ADDRESS>,  Alex Fiannaca  Google,
Inc.Seattle, WashingtonUnited States<EMAIL_ADDRESS>,  Pedro Vergani
Google, Inc.LondonUnited Kingdom<EMAIL_ADDRESS>,  Chinmay Kulkarni
Google, Inc.Atlanta, GeorgiaUnited States<EMAIL_ADDRESS>,  Carrie Cai
Google, Inc.Mountain View, CaliforniaUnited States<EMAIL_ADDRESS>and
Michael Terry  Google, Inc.Seattle, WashingtonUnited States
<EMAIL_ADDRESS>
(2018)

###### Abstract.

This paper examines the art practices, artwork, and motivations of prolific
users of the latest generation of text-to-image models. Through interviews,
observations, and a user survey, we present a sampling of the artistic styles
and describe the developed community of practice around generative AI. We find
that: 1) the text prompt and the resulting image can be considered
collectively as an art piece (prompts as art), and 2) prompt templates
(prompts with “slots” for others to fill in with their own words) are
developed to create generative art styles. We discover that the value placed
by this community on unique outputs leads to artists seeking specialized
vocabulary to produce distinctive art pieces (e.g., by reading architectural
blogs to find phrases to describe images). We also find that some artists use
“glitches” in the model that can be turned into artistic styles of their own
right. From these findings, we outline specific implications for design
regarding future prompting and image editing options.

AI art, Artists using AI, Text-to-Image models

††copyright: acmcopyright††journalyear: 2018††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation emai; June 03–05, 2018; Woodstock, NY††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Human-centered computing Empirical
studies in HCI Figure 1. Prompt artists develop descriptive text-based prompts
that are rendered by text-to-image models. Highly skilled prompt artists will
develop 1) distinct visual concepts and styles (S1), 2) prompts that can also
serve as titles of the art piece (“prompts as art”, A1), and 3) “prompt
templates” (A2), which encapsulate specific visual concepts to be customized
by others. Artists strive to discover unique natural language that produces
unique visual outputs (G1), and/or model “glitches” (G2) that can be elevated
to artistic styles in their own right. Finally, some prompt artists validate
the novelty of their work by conducting an image search for similar images
(C1).

##  1\. Introduction

Advances in text-to-image(TTI) models have led to significant improvements in
the quality of computer-generated, synthetic images (Dhariwal and Nichol,
2021; Ho et al., 2022). A new generation of text-to-image models enable the
creation of high-fidelity images via descriptive text prompts by leveraging
advances in large language models (OpenAI, [n.d.]a; Mid, [n.d.]; Saharia et
al., 2022; Yu et al., 2022; Rombach et al., 2022). With broadening access to
these models, communities of practice have emerged, enabling people to share
designs, prompts, and example images. For instance, there are now tools to
help people write prompts 111https://promptomania.com/prompt-builder/, and
even marketplaces for successful prompts 222https://promptbase.com/shop.

Prior work has examined the phenomenon of computer-generated art in a variety
of contexts (Ch’ng, 2019; Agüera y Arcas, 2017; Mazzone and Elgammal, 2019).
For example, as an art historian analyzing the AI-assisted art movement,
Mazzone et al. (Mazzone and Elgammal, 2019) describe how the artist’s role has
adapted to include pre-curation, tweaking, and post-curation. More recently,
Hertzmann argues that text-to-image (TTI) models like DALL·E do not themselves
create art, but that the artists and technologists who apply them as tools are
the ones creating art (Hertzmann, 2020). With the emergence of this new class
of models, which are capable of producing extremely high quality images from
textual descriptions (e.g., (Par, [n.d.]; Ima, [n.d.]; OpenAI, [n.d.]a)), we
are motivated to understand how this new technology is being adopted and used
by creators.

In this research, we provide a snapshot of a vibrant community of art practice
that has arisen around text-to-image models,  sharing insights into the
ingenuity and creativity of the users of these models.333We thank our
anonymous reviewers for the specific phrase recognizing our contribution..
Within a US-based technology company that has produced its own TTI models, we
sent a survey to the TTI models users to gain a basic understanding of how and
why they are used. We also interviewed and observed 11 prominent users of
these models who are using the models as an art medium, recruiting from survey
respondents and by directly asking prominent users. They have each generated
thousands of images with both the internal and other publicly available
models, and are actively sharing their creations in multiple communities. In
studying the artistically-driven members of this local community, we sought to
understand their practices, their artifacts, and their motivations for
engaging extensively with the models. For the purposes of this research, we
scope our inquiry to studying interfaces that only accept text as input
(recognizing that a wide variety of model capabilities and interfaces are
available, including those that enable more fine-grained editing of images).
We restrict our scope to text-only interfaces because these were the first
interfaces available for models such as DALL-E, and these text-based
interfaces have seen considerable use by the public and our internal users.

Our study reveals that users of these models have developed a range of
artistic styles, including origami figurines, fashion (e.g., dresses) made out
of materials like bricks, and reality “mash-ups” that create hybrids of
animals or of fruits and vegetables (see Figures 2(b), 3(a), 3(b), 4(b)).
However, we also found that the artistic outputs of this community of users
are not limited to the images themselves. For example, the prompt itself is an
important output, and a piece of the art: a parsimonious, descriptive prompt
accompanying the image is seen as a virtuous goal beyond just the image, as it
simultaneously acts as a “title” for, and description of, the art piece (see
Figure 1, A1). Similarly, a prompt template—a text prompt with one or more
empty “slots” for others to fill in—is considered an art piece all on its own
(see Figure 1, A2). Among other characteristics, a well-designed prompt
template has the property of encapsulating an artistic vision that can
nonetheless be customized by future users of that prompt template.

Our results also reveal the lengths some users go to when searching for
unique, distinctive outputs. In particular, some creators turn to thesauri or
online, domain-specific blogs (e.g., architectural blogs) in search of
vocabulary that elevates the model output beyond the ordinary. This focus on
vocabulary suggests that capable TTI model artists may also benefit from being
highly skilled with natural language. Another creator explicitly seeks unique
model outputs, but through identification of “glitches” that can be elevated
to styles all on their own. For example, this latter artist found the model
did not render reflections in mirrors perfectly, and explored this concept
through a number of pieces (see Figure 1, G2).

Finally, we find that the artists interviewed place a premium on originality,
with some turning to image search to validate that their outputs are, in fact,
unique.

In sum, this paper presents results from a survey and interview study of heavy
users of TTI models, making the following contributions:

  * •

Usage summary: From survey data from 161 respondents, we find that when they
use a model, 20% of respondents report using a TTI model for one or more hours
at a time, indicating fairly sustained use of these models by a sizable
portion of the community surveyed.

  * •

Sample styles: We provide a sampling of artistic styles developed by study
participants to contextualize the types of outputs being produced by new text-
to-image models.

  * •

Prompt as art: We find that the prompt itself is often considered a part of
the artistic output (in addition to the actual image), with artists pursuing a
goal of creating parsimonious, descriptive input prompts.

  * •

Prompt templates as art: We discover that artists also produce prompt
templates to encapsulate a unique visual concept that others can customize.

  * •

Natural language mastery for visual language artistry: We describe how TTI
artists seek unique natural language in an attempt to elevate their pieces
beyond the norm.

  * •

Glitches as art: We show how some artists look for “glitches” that can be
reliably transformed into new styles.

  * •

Validating originality: We describe artists’ concerns in validating their
outputs as original, and how they currently validate through image search.

Together, these findings suggest new directions for interactive interfaces and
aids for prompt-centric uses of TTI models: 1) Methods and tools to help users
locate novel language and capabilities of the model, 2) aiding users in
validating the originality of their outputs, and 3) reifying the notion of a
prompt template into a standalone computational artifact that supports richer
interaction by users of the template. Importantly, while our results derive
from a study of internal TTI models, the implications for design are generally
applicable to use of any TTI model (e.g., the notion of a prompt template is
useful for any TTI model, as it captures a particular artistic vision in a
portable, yet customizable. form).

In the rest of the paper, we review related work, describe our study method,
present results from the survey and interview study, and conclude with a
discussion that draws implications for design from the study data.

##  2\. Related Work

Advances in deep learning have led to the development of generative machine
learning models capable of producing both images that are highly realistic and
images that are highly creative in both existing and novel artistic styles
(Cetinic and She, 2022). For example, Generative Adversarial Networks (GANs)
(Goodfellow et al., 2020) learn to generate images and simultaneously
distinguish real and fake images to generate highly realistic images. Many
variations of the GAN architecture have been investigated (e.g., (Zhu et al.,
2017; Karras et al., 2019; Brock et al., 2018)). A particularly relevant
variation of this architecture is the Creative Adversarial Network (CAN) from
Elgammal et al. (Elgammal et al., 2017), designed to generate images with
novel artistic styles. This artwork was subsequently featured in multiple
exhibitions (AIC, [n.d.]) where human observers could not distinguish the CAN-
generated art from human-authored artwork. Outside of GANs, Gatys et al.
(Gatys et al., 2015) introduced a method to apply learned artistic styles to
random images (a technique now known as Neural Style Transfer (Jing et al.,
2020)). Additionally, work originally designed to make convolutional neural
networks more explainable, now referred to as Deep Dreams, became popular for
generating art (McFarland, 2016) due to its ability to generate psychedelic
versions of images (Mordvintsev et al., 2015). While these techniques allowed
for generating images with creative and novel artistic styles (Elgammal et
al., 2017), none provided significant affordances to end-users for controlling
what was generated outside of the training data scope.

Mansimov et al. (Mansimov et al., 2015) addressed this issue by showing that a
generative model could produce novel images from natural text when conditioned
on image captions. As text-to-image models rely on language modeling
techniques, recent advances in the scaling of large language models (Devlin et
al., 2019; Raffel et al., 2020) have enabled the development of
correspondingly large text-to-image models with impressive results. The most
recent of these models include: DALL·E (Ramesh et al., 2021; OpenAI, [n.d.]b)
and DALL·E 2 (Ramesh et al., 2022; OpenAI, [n.d.]a), Stable Diffusion (Rombach
et al., 2022; Sta, [n.d.]), Midjourney(Mid, [n.d.]), Parti (Yu et al., 2022;
Par, [n.d.]), and Imagen (Saharia et al., 2022; Ima, [n.d.]).

Fueled by the latest advances in text-to-image models, current image
generation applications are becoming mainstream. With this broader adoption
comes the question of how these new models’ capabilities impact art practices,
which we examine in this paper.

###  2.1. AI in Creativity Support Tools & Human-AI Co-Creation

AI tools have played a prominent role in creativity support tool (CST)
research (Frich et al., 2019; Hwang, 2022). AI-based CST systems have been
produced to support artistic generation in domains such as fashion and product
design (Sbai et al., 2018; Jeon et al., 2021; Quanz et al., 2020), music
creation (McCormack et al., 2019; Louie et al., 2020; Huang et al., 2020),
drawing (Davis et al., 2015, 2016; Oh et al., 2018; Karimi et al., 2019),
visual design and story-boarding (Zhao et al., 2020; Shi et al., 2020), and
storytelling (Hodhod and Magerko, 2016; Perrone and Edwards, 2019). Most of
these tools support the artistic implementation process as either production
aids (i.e., tools that perform most of the work of generating the art, e.g.,
generative text-to-image models) or as execution aids (e.g., AI-powered brush
tools in drawing applications) (Chung et al., 2021b).

Hwang et al. (Hwang, 2022) further characterize how these tools apply AI
models in the creative process as falling into four general categories:
editors (facilitate execution of processes), transformers (aid in changing
existing content), blenders (combine 2 or more content sources), and
generators (produce novel content). In this framing, the large-scale TTI
models that this work is focused on fall into the generators category.

Additionally, research in the related field of Human-AI Co-Creation (a sub-
field of Mixed-Initiative Co-Creativity (Yannakakis et al., 2014)) is highly
relevant. In the Library of Mixed-Initiative Creative Interfaces (Spoto and
Oleynik, [n.d.]), Spoto et al. proposed a framework to understand mixed-
initiative co-creation as a process involving seven potential actions: ideate,
constrain, produce, suggest, select, assess, and adapt. Muller et al. (Muller
et al., 2020) extended this framework to generative AI applications, while
Grabe et al. further simplified this extension and characterized four primary
interaction patterns concerning GAN applications: curating, exploring,
evolving, and conditioning (Grabe et al., 2022). In our work, we observe
similar themes, especially the notion of users of TTI models feeling like they
are using the models to explore, and acting as curators of their outputs.

Work in this field has also identified core challenges in creating human-AI
co-creation systems. For example, Chung et al. (Chung et al., 2021a)
identified the limited ability to control the output of generative AI models
and proposed using gestural input to constrain/guide the model output.
Likewise, Buschek et al. (Buschek et al., 2021) identified a set of nine
challenges system designers could encounter when developing human-AI co-
creative systems. In the context of TTI models, they identified challenges of
invisible AI boundaries (“A (generative) AI component imposes unknown
restrictions on creativity and exploration”) and conflicts of territory (“AI
overwrites what the user has manually created/edited”) as particularly
salient. Participants in our study encountered similar challenges of
identifying what the models are capable (and not capable) of, and in building
upon prior inputs to the system.

Building on this prior CST research, we examine the emergent practices and
goals that have evolved in concert with the latest generation of TTI models.

###  2.2. Generative AI as an Artistic Medium

Given the rapid advancement of AI models for generating images with ever more
creative and novel styles, art historians and technologists have been actively
discussing how to conceptualize AI-assisted art in relation to other artforms.
Experts in these communities have disagreed as to whether generative models
should be considered artists in-and-of themselves (Mazzone and Elgammal, 2019)
(as with the famous sale of Portrait of Edmond Belamy auctioned by Christie’s
in 2018 (Christie’s, 2018)) or whether they should be considered as merely a
tool employed by artists. Hertzmann (Hertzmann, 2018, 2020) argues that
generative AI models are similar to the camera as it relates to the art of
photography: a tool the enables the art. Hertzmann further theorizes that “art
is an interaction between social agents,” and generative AI models are
therefore best considered an agent in this interaction. Agüera y Arcas (Agüera
y Arcas, 2017) provides a similar argument with an in-depth discussion of the
similarity between the emerging field of AI-generated art and photography,
particularly surrounding the historical reaction of painters to the
introduction of the camera. Grba (Grba, 2021, 2022) provides a framework to
critically evaluate art created with a generative AI model. In this framework,
he echoes arguments above, and critiques what he refers to as “the ever-
receding artist”: the repeated occurrence of technologists referring to models
as artists, thereby minimizing the contribution of the human who employed the
model to create art. Finally, Browne (Browne, 2022) explored what it means to
be an “AI artist” and proposed the framing of an AI artist as a bricoleur
(building upon (Grba, 2019)), saying, “Bricolage is common to generative art,
where ideas are developed through playful experimentation with existing tools
and techniques.”

In our paper, we present an analysis of interviews with artists who employ a
large TTI model for the generation of their art, highlighting the work of
three of these artists and provide context around their motivations and goals.
In our results, we find thematic alignment with perspectives advanced by
Hertmann, Agüera y Arcas, and Grba above: While the models can produce
surprisingly high quality output, dedicated users of these models employ the
models as tools to explore specific themes and concepts. Accordingly, they
have intentionally developed processes (e.g., locating domain-specific
terminology) to improve their ability to achieve their individual goals.

###  2.3. Diffusion and Auto-Regressive Models

Diffusion models (e.g., Imagen (Ima, [n.d.]) or DALL-E (OpenAI, [n.d.]a)) are
trained by gradually adding noise to an image, until all of the image is
completely noise. The model then learns to reverse the noising process to
generate the original image. In this way, a diffusion model learns to
synthesize an image from noisy images, and is capable of generating images
from arbitrary “noise.” For text-to-image models, the models are also trained
(conditioned) on text inputs (nichol2021glide), allowing the model to produce
an image from a noisy image input and text input, where the resulting image
bears a resemblance to the input text.

Autoregressive models, such as Parti (Par, [n.d.]), treat text-to-image
generation as a sequence-to-sequence problem, akin to machine translation or
other language modeling tasks. In the case of TTI models, the “translation” is
from text to image (i.e., text tokens to image tokens).

In our study, participants used both types of models.

##  3\. Study Design

To understand current practices, motivations, and goals when using modern
text-to-image models, we sent a survey to internal users of two internal TTI
models to collect basic information about their use of these models (e.g.,
time spent using the models, motivations, desired capabilities, and prompting
strategies). We also interviewed and observed 11 power users of TTI models (8
identifying as Male and 3 identifying as Female) in a 50-minute study to
uncover their motivations and practices. The latter participants were prolific
users of one or more TTI models. Models used by the participants in the study
are anonymized for review, but are of the same basic capability as state of
the art text to image generation models such as Imagen (Ima, [n.d.]), Parti
(Par, [n.d.]), and DALL-E 2 (OpenAI, [n.d.]a) which are described in more
detail in our related work.

###  3.1. Participants

For the survey, participants were recruited from an internal chat channel
dedicated to TTI models (where the internal chat channel has thousands of
members) and internal TTI model mailing lists (with hundreds of members). From
the survey respondents, we identified interview candidates who had reported
having created artwork over more than ten sessions and having spent more than
five hours in the previous week using a TTI model. To create a pool of
participants, we recruited eight of these latter respondents, and further
recruited three prominent artists in the internal artist community to
participate. These three artists are quite visible in the internal artist
community, and have shared their unique artwork collections within that
community. Participants were also actively engaged in external communities,
sharing knowledge, expertise, and artwork. Participants were given a 60 USD
gift card for participation.

###  3.2. Interview structure

The study consisted of four parts intended to understand participants’
practices. Each participant was first asked to create an image of their choice
to allow the researcher to observe their natural practices. In the second part
of the study, participants were asked to reflect on 1) an artwork they were
proud of, 2) a piece they found most successful, and 3) the piece that was
least successful. In the third part of the interview, participants were asked
to reflect on someone else’s work by examining only the prompt, and
specifically asked to either improve or change the prompt in their style. In
the last part of the interview, participants were asked to discuss envisioned
uses for these text-to-image models.

Interviews were conducted remotely. We recorded the shared screen and
automatically generated transcripts for the interviews. We constrained our
focus to interfaces that only use text prompts as input to the models. In
addition, some participants voluntarily shared their collection of generated
artwork after the interview.

###  3.3. Qualitative Data Analysis

For the qualitative analyses, the authors analyzed the video transcriptions
and also noted comments on participants non-verbal interactions. The final
corpus of automatically generated transcripts was 164 pages (60614 words). The
first two authors each reviewed the transcripts data independently, looking
for ways of explaining the artistic practices (Miles and Huberman, 1984). In
this process, the authors separately analyzed each transcript to extracted
salient themes, and independently generated hypotheses and points of
discussions (Braun and Clarke, 2006). Using these data, all authors
participated in two rounds of interpretation sessions to arrive at the primary
themes reported in this paper, and resolve any discrepancies and
disagreements. During the interpretation sessions, authors also analyzed the
prompts and the images created by the study participants to identify unique
artistic styles and practices. These sessions were inspired by existing
analysis practices from qualitative media analysis (altheide2012qualitative).

##  4\. Survey Results

We received 161 responses to the survey. Of these responses, 160 answered the
question, “At present, why are you using the [TTI] models?” Of the responses
to this question, 79 (49%) indicated they use the models to create art, 33
(21%) reported using it as part of their creative work pipeline, and 126 (79%)
indicated their use was curiosity-driven (not work-related).

In the survey, we also asked participants to estimate the length of time they
work with a TTI model when they use one (“When you interact with a model, how
much time do you typically spend interacting with it?”). We received 157
responses to this question, with 20% of respondents indicating that they use a
model for one or more hours at a time when they use it (11% reporting using it
for 1-2 hours at a time, 9% using it for 2 or more hours at a time), 53%
indicating they use a model for 10 minutes to an hour, and 27% reporting use
for less than 10 minutes.

When asked about observed strengths and weaknesses of the various models
they’ve interacted with, survey responses indicated a number of desired
capabilities, such as the ability to render text in images, the ability to
have more control over spatial arrangements, and the ability for models to
handle complex prompts. We also asked participants for desired capabilities
when interacting with the model. Seventy six percent of the respondents
requested a “how to build a prompt” guide, and 75% desired the ability to fork
and remix images, especially for spatial refinement. Seventy six percent of
the responses also indicated they would like features like bookmarking, and
the ability to directly share outputs to internal chat groups or social media.
As we will see in the artist spotlights below, there is a clear social
component to working with these TTI models for our study participants.

Finally, 63% of the responses desired greater control over the model, such as
the ability to assign specific values to each of the prompt words.

When asked to provide prompting techniques they have learned, common themes
for the strategies included 1) producing specific art styles and eras, such as
“impressionist style”, 2) use of keywords that describe camera lenses and
aperture (e.g., “DSLR photo”,“3D render”,“24 mm, f8, ISO1000”), and 3) domain-
specific terms (e.g., “Line Art”, “black and white”).

##  5\. Prompt Artists: Styles, Motivations, Practices

In this section, we provide a sampling of the vibrant internal TTI artistic
community by spotlighting the work of three highly active creators; Shai Noy,
Irina Blok, Dan Smith. 444In the text, we use the terms “artists,” “creators,”
and “study participants” interchangeably. We denote the three spotlighted
artists as A1, A2, and A3, and other participants by a participant number
(e.g., P1, P2, …, P8).. For these three artists, we describe and present
examples of the styles they have developed and summarize their artistic
motivations and goals. We then provide a summary of motivations, styles, and
practices observed across the 11 interview participants. In the section that
follows, we describe salient high-level, emergent themes arising from the
interviews and observations. We want to credit the artists whose artwork are
featured, and they had expressed the desire to be associated with their
artwork. We use their full name in places where the artwork appears.

###  5.1. Shai Noy: The Explorer (A1)

Shai Noy is a software engineer with no training in the visual arts or design.
However, they have produced thousands of images with TTI models. The styles
developed by this artist include “super macro photography” images (i.e.,
extremely close-up views of objects, Figure 2(a)), and fashion (dresses,
suits) made out of unusual materials, such as wood, grass, brick, or ice
(Figure 2(b)).

Elements of both discovery and community were emphasized as rewarding for this
artist, such as being the first to explore particular concepts and the ability
to share discoveries: “Everything is more fun when you can share it” and, “Art
doesn’t live in a vacuum, nobody starts from scratch, everything is based on
something else. I am proud of being able to recognize the potential” (A1).

(a) Super macro photography

(b) Dresses in various unusual materials

Figure 2. Selections from Shai Noy (A1 - The Explorer)

###  5.2. Irina Blok: The Art Director (A2)

Irina Blok is a designer who has done visual work for many years using stock
images and applications like Photoshop. This artist’s styles include origami
dancers (Figure 3(a)) and reality “mash-ups,” such as sliced produce that
contains different textures internally (e.g., a sliced head of cabbage that
reveals a cross-section of an orange, Figure 3(b)).

Driving these styles is a passion for developing “impossible objects,
something we haven’t seen before, mashups” (A2). They also seek to create
images that differ significantly from the images it was trained on: “The
further away the generated images are from what it was trained on, the more
the satisfaction” and, “they’re [the resulting images] also defying the rules
of its training set, like defying […] gravity. [..] A creative prompt breaks
that” (A2).

Importantly, this artist’s output also includes prompt templates that describe
a particular, parameterized image or visual concept, such as this prompt for
generating a house: Audacious and whimsical fantasy house shaped like ¡object¿
with windows and doors, ¡location¿. These prompt templates are carefully
crafted to reliably produce pleasing results for others. We describe this
concept more fully in a later section.

Notably, when working with the text-to-image models, A2 considers the model to
be akin to an artist itself, with themselves the art director. In this
relationship, A2 acknowledges a certain lack of control: “[I] don’t have full
control, and there’s beauty in this” (A2). At the same time, they also
consider the model to be a tool: “[The model] is a brush […] you just learn to
speak its language” (A2). With these dual views (model as an artist, model as
a tool), they note that “the hardest part is [the] conceptual aspect, being
skillful with [the] prompt” and that “it’s a thought exercise, it’s not a
visual exercise” (A2). It’s really about how to make people think like an
artist” (A2). In this latter sentiment, this participant was specifically
speaking to the need to think like an artist in formulating a prompt, as
opposed to formulating a prompt as if one was talking to a machine.

(a) Origami dancers

(b) Reality mash-ups

Figure 3. Selections from Irina Blok, (A2 - The Art Director)

###  5.3. Dan Smith: The Social Commentator (A3)

Dan Smith comes from a background in visual media, and is partially driven by
the desire to deliver a message about the climate crisis, in order to
facilitate change and awareness: “[I’m] not just having fun … but [actually]
making something that has some power” (A3). In working towards this goal, they
want to make “something that you look at, and it makes you feel something”
while also making “something that people would want to look at” (A3). A3’s
image styles align with these overall goals: They have a number of pieces that
put nature in “situations where you wouldn’t see it” (Figure 4(a)) and have
created numerous “hybrid animals” (Figure 4(b)). This particular style—hybrid
animals—is also inline with a motivation to create images that “would be hard
to […] visualize or create, if you were […] a really skilled Photoshop artist”
(see Figure 4).

A3 also heavily considers the quality of the image when assessing its outputs:
it must have near expert-level composition, photorealism, and/or artistry.
When describing the artwork they created and how they created them, they
noted, “I think the ones that I pick out are […] standouts for various
reasons, and just […] like what I said, […] composition photorealism, artistic
quality” (A3). Consistent with their emphasis on overall image quality, they
have found ways to address undesirable outputs. For example, in generating
images that include animals, they found they needed to adopt a specific
strategy to create aesthetically pleasing images: “I would do ‘Tall Grass’ a
lot because early on I discovered that limbs and fingers and paws can get a
little wonky” (A3). The “tall grass” addition was their creative strategy to
hide feet or paws and suggests an understanding of model limitations, but also
a sense of how to cope with these limitations.

(a) Climate change

(b) Hybrid animals

Figure 4. Selections from Dan Smith (A3 - The Social Commentator)

###  5.4. Summarizing Motivations, Styles, and Practices

One of the primary motivations for interacting with the models was that
participants found them fun—the models’ output quality enabled people to feel
creative, and they were generally interested in interacting with this new
class of model. Some people also noted that the model enabled them to engage
in their domain interests in a new way. For example, one participant said that
the models allowed them to explore their interest in Swiss trains with the
model, while another found it compelling to try to create new forms of
currency (e.g., new types of coins). One participant used it to create the
equivalent of clip art for presentations: “I use [TTI] model when I need some
[…] clipart to use in my presentation, and [TTI] model would be amazing for
that…” (P2).

In comparing the artists spotlighted above to the other participants, one
notable difference in practices is that the spotlighted artists placed a
particular emphasis on exploring specific themes in depth (e.g., origami
figurines). The other participants did not pursue concepts with the same rigor
or depth.

##  6\. Themes: Art Beyond Images, Discovering Unique Points, and Validating
Originality

Across our interviews and observations, a number of themes emerged. First, the
notion of what constituted the final artistic output was not always just an
image: Some creators consider the prompt itself as part of the art. Similarly,
a prompt template can be considered an artistic output. Second, there was a
clear desire for creators to discover new styles possible with the models.
This focus on originality extended to one creator going so far as to conduct
an image search on successful outputs to validate their originality. We unpack
these themes further in this section: 1) Prompt as art, 2) Prompt template as
art, 3) Discovering new capabilities of the model, and 4) Validating
originality.

###  6.1. Prompt as Art

For some participants, the prompt itself was part of the overall art, and thus
worthy of attention. For these participants, it was important to “[create]
aesthetically pleasing images” and “[develop] art concepts” that were
inherently tied to prompts. We detail these motivations and behaviors below.

While generating aesthetically pleasing, “glitch-free” images was a common
goal of the creators, other goals were also present in their practices. For
some participants, the prompt itself was part of the overall art, and thus
worthy of attention 1) on its own, and 2) as it relates to the image: “It’s
part of the aesthetic” (P3), where the prompt is “like a title of the piece,
but you don’t get to choose it independently” (P3). Hinted at in this last
comment is the notion that while a prompt could also serve as a title of the
art piece, there is a clear dependency on, and perfunctory role for, that
prompt as well: The prompt serves as the source material for the model
generating the resultant image. Given this dependency, finding a prompt that
produces the desired result and that can serve as a title for the piece can be
challenging, but rewarding when it happens.

(a) “a photo of an owl turning into a croissant. f2.2”

(b) “a photo of a bananasaurus rex. a bananasaurus rex has the legs and arms
of a banana and the body and head of a tyrannosaurus rex. sigma 85mm f2.2.
studio lighting.”

Figure 5. Artist Ian Fischer (P3) considers (a) as a successful art work given
the brevity of the prompt, while (b) a failed attempt because the prompt is
too long to get the image they want

This same artist (P3) further described the prompt’s role as “communicating
the image, and the idea of the image, and how I got it all at the same time”
(P3). This quote sheds light on how art with TTI models can, in some sense, be
considered multimodal (text and image) for both the artist and viewer: The
prompt and the final output combine into a single, mutually-reinforcing, art
piece. Seen in this light, one can consider the prompt as art itself—a well-
crafted prompt that creates a compelling image but also accompanies that
image, saying something about the image. See Figure 5a for an example of
“prompt as art,” as well a prompt that wasn’t able to achieve this same level
of aesthetic (Figure 5b).

Diving deeper into this theme, we observed that the artists believe the
“concept” is a critical component of an artwork. In an internal blog article,
A2 describes, “There’s a common misconception art is largely about drawing and
painting skills. Art is not only about how something looks, it’s about what it
says, it tells a story, and has a concept. Art can surprise, provoke, teach,
delight and inspire. Art is not just about drawing, art is a way of thinking.”
A1 also suggested “the unit of shareable artwork is not necessarily a specific
image but maybe it’s the whole exploration of the concept of those images.”

In pursuing “prompt as art,” we observed participants impose different
constraints when creating their prompts, with some wanting it as descriptive
as possible, while others attempting to make it as simple as possible. One
participant even discovered delight in their accidental discovery that their
“random string” produced beautiful output, and named that prompt as their
proudest achievement. In this latter case, the pride comes from the joint
pairing of the random string and the beautiful output—without the context of
the random string, the image has less value, as the random string reveals an
unexpected feature of the model.

###  6.2. Prompt Templates As Art

Figure 6. Example of the prompt “Audacious and whimsical fantasy house shaped
like ¡object¿ with windows and doors, ¡location¿”. All images above use
“strawberry” as the shape, with the location varied (“countryside”, “Paris”,
“Tokyo”).

In a spirit similar to “prompt as art,” five artists sought to produce prompt
templates. A prompt template is an image description with “slots” for someone
else to fill in. For example, we previously noted this prompt template created
by A2: Audacious and whimsical fantasy house shaped like ¡object¿ with windows
and doors, ¡location¿ (see Figure 6 for example outputs of this prompt
template).

These prompt templates leverage the capabilities of the models as well as the
lightweight, accessible features of prompts. More specifically, when an artist
identifies a compelling composition, they can create a text prompt that allows
others to create a similar composition, but with their own unique
customization to it. The ease with which the templates can be shared also
introduces social motivations for producing and distributing templates (e.g.,
to participate and contribute to a larger community of practice). We elaborate
on these points below.

Prompt templates have a number of key features:

  * •

They are tightly coupled to and represent a particular artistic concept,
vision, or composition (such as a “whimsical fantasy house”). These features
make prompt templates conceptually richer than words or phrases used to
specify stylistic characteristics of the image (e.g., “35mm” or “watercolors”,
which may be used to produce a particular effect, but don’t specify a larger
composition).

  * •

Others can make use of prompt templates by filling in the blanks. The richness
of the models means that the templates guide the overall generated image, but
users’ unique input can yield a diverse variety of outputs.

  * •

There is the intent for the prompt template to provide consistently high
quality and delightful output when used by others.

Unpacking these concepts in the context of the example prompt above, the
skeleton of this prompt embodies a particular (visual) concept: A fantasy home
in a given location. Someone making use of this prompt can customize it
through two key variables: A shape for the house and a location for the house.
While these are seemingly simple variables to customize, the template gives
the user great flexibility in terms of the final outputs produced by the
model: Any number of shapes can be provided in any number of locations (in
fact, the user is free to substitute any text in the slots they wish).

Simultaneously, the prompt cues the model to the types of output to produce,
as well as details to guide the generation. The phrase “audacious and
whimsical fantasy house” defines desired attributes of the house, while the
specification of “with windows and doors” provides additional details that
should be included in the generated house. These details in the prompt help
increase the reliability of the model output and reduce the likelihood that
the downstream user of the prompt template needs to experiment further with
the overall prompt structure and content to obtain a good output.

A noteworthy characteristic of these templates and the text-to-image models is
the rich interplay that results between the original template and the phrases
the user substitutes in the template. For example, a particular choice of
location can profoundly influence the resulting house generated by the
model—the model does not simply generate the same type of house and situate it
in a different location. Instead, the choice of location can directly interact
with the other parts of the prompt. For example, creating a strawberry house
in the “countryside”, “Paris”, or “Tokyo” yields qualitatively different
outputs for the house, with the house style meshing more naturally in the
chosen location (e.g., having styles of windows more typical for a European
building for the house in Paris, and doors more typical of Japan for Tokyo—see
Figure 6). These interactions between the template and the users’ choices
enable a diverse variety of outputs that allow a user to explore a wide range
of ideas.

To produce prompt templates, one participant described a process whereby they
would 1) input a prompt, 2) identify high-quality outcomes, and 3) rewrite the
prompt to try to produce that same outcome again. This process could involve
several iterations until they get a reliable prompt. Once a prompt is working,
participants would sometimes remove content to make it more succinct. For
example, they would first emphasize a specific characteristic (like “high
resolution”) by applying many descriptors representing a similar effect (such
as “high res”, “DSLR”, “crystal clear”, “photo realistic”), then start
removing the repeated qualifiers in the prompt until the prompt could reliably
generate similar outputs to the original, verbose version.

As with the notion of a “prompt as art,” the creation of these prompt
templates can also be considered an artistic outcome in and of itself: The
prompt author must first develop a compelling concept, then ensure it has
enough capacity to enable people to produce their own unique creations within
the frame of the concept. When done well, a prompt template has the quality of
attracting users (because of the compelling output produced by the prompt) and
continually delighting users with the outputs produced using their unique
input.

Elaborating further on this notion of “prompt template as art,” a prompt
template represents a particular artistic vision, with the prompt text
capturing the overall design, composition, and aesthetic intentions of the
author. Notably, because the artistic vision is captured in natural language
text, a prompt template can be used across TTI models: Users can customize the
template as desired, then tweak the prompt to produce the desired output using
whatever model they have at their disposal.

###  6.3. Discovering Unique Points in the Model’s Latent Landscape

A common goal of many artists was to discover new capabilities of the model
that others had not yet found. As one participant put it, they tried to
“break” the model, pushing it to its limits. As with prompt templates, these
newly discovered capabilities are often shared so others can apply the concept
in their own images. For example, an artist may identify the ability of the
model to produce physical sculptures out of unusual materials or to create
origami-like figurines (as A2 did). Once a concept and ability have been
identified, others can build on the core idea and create their own images or
permutations. In particular, participants felt proud when they “made the model
do X (new concept)”, especially when they discovered a model capability for
the first time in the community in which they heavily engage, as the discovery
could influence the discourse in the community. We expand on these points
below.

new words to steer the model in new directions. For example, one artist
mentioned referencing architectural blogs to learn that domain’s vocabulary so
it could be applied to their prompts (A2). Another artist mentioned turning to
a thesaurus to enrich their vocabulary for the prompt. As one concrete
example, A1 (the “Explorer”) likes to employ “unusual words” to steer the
model, such as “intricate” instead of “detailed”, explaining: “If you choose
common words, then you get a bit of an uninspiring result quite often. But if
you use something a bit more unusual, then you really narrow down […] the set
[of images], and you’re going to get into things that use this less common
word” (A1).

What’s noteworthy here is that in the pursuit of novel imagery, creators would
carefully research and choose words to produce specific visual outcomes: What
you say and how you say it is critical to producing high-quality imagery with
text-to-image models, requiring TTI users to enrich their natural language
vocabulary in order to develop and skillfully execute a unique visual
language.

Driving these practices of discovering new capabilities was a clear desire to
push the model away from “average” outputs and get it into more unique spaces.
In this sense, the artists are navigating and charting the vast latent space
of the model and sharing back the most interesting places discovered. When a
new, unique output space was discovered, artists expressed a certain
satisfaction in having discovered that space: “ A good rule of thumb is to be
more descriptive than not […] if you see that something is lacking, then you
try to add more descriptors that will encourage the model, in this direction
[…] sometimes you just have to reword a sentence or move something from one
sentence to another […] it’s mostly like binary search” (A1); “ I think your
choice of vocabulary is very interesting because you want a large tree. But
then, instead of saying a large tree you say mature tree […] Where do these
[…] choices come from? They just come from trial and error” (A2).

In seeking unique spaces, one participant (P8) expressly hunted for
interesting imperfections; instead of avoiding glitches, they sought glitches.
For example, this participant found one of their prompts created imperfections
in its output for “a hybrid of a clock and a snail on an infinite mirror.
Steampunk. DSLR photo. astrophotography” (Figure 7). Here, A4 explores the
imperfections of the infinite mirror: it’s “technically a failure but still
amazing” (P8). P8 also discovered that the model sometimes has trouble
generating the backs of things (like cats) and developed that behavior into
its own art style (Figure 7).

Figure 7. Artwork by Paul Emmerich(P8). Examples of “breaking the model”: the
artist seeks out and elevates “glitches” in the model’s output to create new
styles. The artist takes advantage of the model’s imperfect reflections (on
the left) while making creative use of its tendency to transform the backs of
cats into cat faces (see the bottom part of the cat’s back, which looks like a
cat’s face).

###  6.4. Validating Originality

One question that often arises on the topic of human-in-the-loop, AI-generated
art is one of creativity and originality—how much can be attributed to the AI
versus the person (Hertzmann, 2020). Our participants also struggled with this
question, with some seeking to ensure their outputs were novel. Participants
wanted to validate the originality of the artistic concept, but could only
check the originality of the artifact. For example, one participant described
a practice of using Google’s image search on compelling outputs to ensure
originality: After producing a creative output, they use image search to
search for that image (or similar images) to ensure it is, in fact, original.
This participant mentioned they do this in part because they are sensitized to
the fact that machine learning models can sometimes memorize portions of the
training data. Given this, they want to ensure their output is not in the
training data.

##  7\. Discussion

In this research, we have provided a snapshot of the emerging artistic scene
enabled by the latest generation of text-to-image models. Our interviews and
observations document the types of imagery artists are developing with these
new models, as well as an enhanced understanding of the types of results
creators seek beyond the images themselves (e.g., prompts and prompt templates
as important artifacts in their own right).

In this section, we discuss some implications deriving from artists’ goals of
seeking novelty, validating originality, and producing reusable prompt
templates. In reviewing design implications for these models, we note that
there are countless ways the input and editing interfaces could be improved
for interacting with these models (for example, one can find many proposals
and working demos online). In this discussion, we restrict ourselves to
interfaces that only take text as input, with no post-processing of the image
(such techniques that allow in-filling of regions by the model). We limit our
discussion to this input modality in part because a number of the artists
interviewed were seemingly attracted to the simplicity (and challenge) that
this single input modality provides.

###  7.1. Aiding Novelty

In our interviews, the use of thesauri and domain-specific blogs (e.g.,
architectural blogs) illustrate the desire of artists to identify unique terms
that help them produce results that rise above the average. Embedding these
search capabilities directly into the tooling could be useful (e.g., quick
access to a thesaurus or an embedded search engine). Pushing this idea
further, there may also be opportunities to make use of large language models
(LLMs) and/or the TTI model’s training data to help surface salient terms for
a given topic. For example, an LLM may be able to generate terms-of-art for
architecture using a prompt like this: Here are some terms specific to
describing the architectural design of a house: 1). In our own test with an
LLM 555Specific model anonymized for review, this prompt yielded terms like
entryway, foyer, entry hall, and elevation. To use the training data itself to
identify new spaces, it may be possible to collect terms associated with a
particular topic, such as “house,” then identify other terms that are more
frequently found near that topic in the training data compared to the rest of
the training data (e.g., using a method such as term frequency-inverse
document frequency (TF-IDF)).

To help users understand how unique their word choices are, one could also
visualize the input prompt with respect to how common each individual input
token is. For templates, one could also show what the common terms would be
for filling in the blanks to help people move beyond those common terms to
find more distinct, unique inputs. One possible outcome in helping people find
more novel inputs is that their inputs may lie outside of the training
distribution, leading to unpredictable results. Providing feedback through
mechanisms like visualizations (e.g., showing frequency in the training data)
could help users better understand these types of issues should they arise.

###  7.2. Validating Originality

As mentioned, there is a desire to validate the originality of the image
produced by the model. Streamlining the process of doing an image search with
an online search engine is one obvious way to address this issue. However, as
is the case in aiding novelty (described above), there may be opportunities to
take advantage of the training data itself. More specifically, in addition to
external search, one could also search for the closest images in the training
data to the image produced.

While the above mechanisms would help validate the outputs generated, there
may also be opportunities to help validate the originality of the input. For
example, one might be able to search the training data to identify which parts
of a prompt exist within the training data.

###  7.3. Materializing Prompt Templates

Prompt templates provide a way for an artist to derive a new style, then share
it with others so they can produce their own unique images. One could imagine
embracing this practice and transforming a prompt template into its own first-
class interface.

For example, one could imagine allowing users to provide multiple inputs for
each slot of a prompt template, then generating the cross-product of all the
inputs. One could also make use of the fact that the prompt text for the
templates is tokenized into vectors. Specifically, by supplying two different
inputs for the same slot, embedding vectors for each input could be obtained
and then automatically interpolated between the embeddings to produce a
spectrum of outputs. For example, for the shape of a house (in the previous
house prompt template), the user could provide two inputs: strawberry and
apple. The system could then produce the embedding vectors for those inputs,
then interpolate between those embeddings to create a series of images that
morph from a strawberry to an apple. However, one thing to keep in mind with
these interpolations is they are occurring in the text space rather than image
space—the model will be interpolating not between shapes (per se) but between
the linguistic concepts of strawberries and apples (which may still produce an
interesting morphing between these conceptual entities).

###  7.4. Prompts and TTI Models as an Art Medium

One the primary ways the three spotlighted artists (A1, A2, and A3)
distinguished themselves from others was their perception of TTI models as an
art medium, with a clear focus on exploring the capabilities and limits of
medium itself, rather than only on individual outcomes. In this spirit, they
embraced the limitation of not being able to edit the images directly,
accepting the text-only input as a defining feature and characteristic of the
medium. For example, A2 expressed “there’s beauty in it” when describing the
prompting interaction with the models. In contrast, other participants focused
more on the outcome, and expressed desire for additional features, such as
direct editing of the generated images. Embracing the TTI models as-is further
reinforces the idea that text prompts are part of the artwork, rather than
simply a means to an end.

This observation suggests that the design implications for TTI models can be
considered from multiple perspectives: prompt-only artists, and creative
professionals using the models to achieve specific goals. Creative
professionals with a specific design goal may require and request specific
features that offer more fine-grained control, perhaps with tools to help
understand model behavior. For example, features related to directly editing
the generated images were among the most frequently requested features in the
survey. Given that non-design experts can also learn to use TTI models to
quickly demonstrate and visualize artifacts, we hypothesize that this may
facilitate more active and iterative communication between design
professionals and their clients, with clients more directly engaged in the
creative process (as opposed to the more traditional pipeline-like design
workflow). If this proves to be true, feedback and collaboration features
could become more important.

###  7.5. Limitations

Our participant pool was drawn among employees of one large US-based
corporation, and does not cover other possible ways that culture, community,
and collaboration might shape use of TTI models (e.g. on social media). Also,
since our analysis was episodic rather than longitudinal, we do not document
how artistic prompting strategies may evolve within individuals. For the
interactions we could observe, observing participants’ interactions with the
TTI model does not definitively indicate their conceptions of how the model
works or how best to prompt it. We also acknowledge that different models
behave in different ways due to their structure, training data, and the design
of the interface. In that regard, we also acknowledge some of the findings
might be model dependent, and are specific to the models used in the study.
Similarities and differences in art forms and art practices might be
observable with different models and communities. Another limitation is that
while our participants are actively involved in multiple communities, we did
not ask participants about their experiences with other models or other
communities in depth.

##  8\. Conclusion

In this paper, we have described a unique moment in time: Recent text-to-image
models have given rise to an exceptionally vibrant community of practice,
complete with new ideas about what constitute notable outcomes (e.g., new
styles, prompts as art, and prompt templates as art). As the larger artistic
and creator community adopts these new models and forms of art, there are
clear ways in which the tools can improve to better support desired practices
including: 1) helping to discover and create novel outputs, 2) providing
methods to validate novelty, and 3) elevating the notion of a prompt template
into a standalone, first class, interactive object. In considering design
implications for these TTI models, our results also suggest the value in
distinguishing between prompt artists—those users who embrace the constraint
of creating images using only an input prompt—and practitioners, who may
desire more fine-grained input and editing controls in comparison.

###### Acknowledgements.

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