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Templating language for generating prompts for text to image generators such as Stable Diffusion

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Dynamic Prompts

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Dynamic prompts is a Python library that provides developers with a flexible and intuitive templating language and tools for generating prompts for text-to-image generators like Stable Diffusion, MidJourney or Dall-e 2. It lets you create and manage sophisticated prompt generation workflows that seamlessly integrate with your existing text-to-image generation pipelines.

It includes:

  • An easy-to-learn templating language that lets you create a number of unique prompts from a single template.
  • Support for wildcard files as placeholders in templates.
  • A mechanism for creating a wildcard library. Text, JSON, and YAML files are supported.
  • Exhaustive generation of all prompts from a template.
  • Variable assignment enabling re-usable prompt snippets.
  • Supports Magic Prompt which automatically spices up your prompt with modifiers
  • Provides an I'm Feeling Lucky feature which uses the semantic search on Lexica.art to find similar prompts.
  • For systems that support attention syntax, Attention Grabber will emphasis random phrases in your prompt.
  • Jinja-powered templating for advanced prompt creation.

The dynamic prompts library powers the Dynamic Prompts extension for Automatic1111.

Table of contents

Quick overview of the templating language:

Variants

{summer|autumn|winter|spring} is coming

Randomly generate one of:

summer is coming
autumn is coming
winter is coming
spring is coming

Choosing multiple variants

This syntax {2$$ and $$A|B|C} will choose two values from the list:

A and B
A and C
B and A
B and C
C and A
C and B

Wildcards

__season__ is coming

Randomly selects a value from season.txt in your wildcard directory.

Let's try a real-world prompt

One prompt template can generate a family of prompts:

Funko pop {yoda|darth vader|jabba the hutt|princess leia|chewbacca|luke skywalker} figurine, made of {wood|plastic|metal|stone}, product studio shot, on a white background, diffused lighting, centered


Now, how about two characters at the same time:

Funko pop {2$$ and $$yoda|darth vader|jabba the hutt|princess leia|chewbacca|luke skywalker} figurine, made of {wood|plastic|metal|stone}, product studio shot, on a white background, diffused lighting, centered


Use whitespace for readability

# Add comments like this
Funko pop
    {2$$ and $$
  	    yoda
		|darth vader
		|jabba the hutt
		|princess leia
		|chewbacca
		|luke skywalker
	}
	figurine, made of
	{
		wood
		|plastic
		|metal
		|stone
	}, product studio shot, on a white background, diffused lighting, centered

Use wildcards for re-usable lists:

# starwars.txt
yoda
darth vader
jabba the hutt
princess leia
chewbacca
luke skywalker
# material.txt
wood
plastic
metal
stone
# studio-shot.txt
product studio shot, on a white background, diffused lighting, centered

Now compose your prompt like this:

Funko pop __starwars__ figurine, made of __material__, __studio-shot__

and easily change it to:

Funko pop __celebrities__ figurine, made of __material__, __studio-shot__

Hat tip to publicprompts for the funko pop prompt.

The complete syntax can be found here.

Installation

pip install dynamicprompts

Additional functionality (see below) can be installed with this command:

pip install "dynamicprompts[magicprompt, attentiongrabber]"

Quick Start

Use the RandomPromptGenerator to create 5 random prompts using a given template:

from dynamicprompts.generators import RandomPromptGenerator

generator = RandomPromptGenerator()
generator.generate("I love {red|green|blue} roses", num_images=5)

>> ['I love blue roses', 'I love red roses', 'I love green roses', 'I love red roses', 'I love red roses']

If you want to use wildcards, instantiate a WildcardManager:

from pathlib import Path
from dynamicprompts.generators import RandomPromptGenerator
from dynamicprompts.wildcards.wildcard_manager import WildcardManager

wm = WildcardManager(Path("/path/to/wildcard/directory"))

generator = RandomPromptGenerator(wildcard_manager=wm)

Assuming you have a file called colours.txt in /path/to/wildcards/directory with one colour per line, e.g.

red
green
blue
purple
yellow

then

generator.generate("I love __colours__ roses", num_prompts)
>> ['I love pink roses', 'I love violet roses', 'I love white roses', 'I love violet roses', 'I love blue roses']

Random seeds

You can pass a random seed in the constructor for predictable outputs

generator = RandomPromptGenerator(wildcard_manager=wm, seed=999)

A list of seeds can also be provided in the generate method.

generator.generate("I love __colours__ roses", num_prompts, seeds=[1,2,3])

In this example, a seed is provided for each prompt generated. The number of seeds must equal the number of prompts, i.e. len(seeds) == num_prompts. If len(seeds) == 1 then the same seed is used for every image.

As a convenience, seeds may also be an int value.

generator.generate("I love __colours__ roses", num_prompts=3, seeds=5)

is equivalent to:

generator.generate("I love __colours__ roses", num_prompts=3, seeds=[5, 5, 5])

Combinatorial Generation

Instead of generating random prompts from a template, combinatorial generation produces every possible prompt from the given string. For example:

I {love|hate} {New York|Chicago} in {June|July|August}

will produce:

I love New York in June
I love New York in July
I love New York in August
I love Chicago in June
I love Chicago in July
I love Chicago in August
I hate New York in June
I hate New York in July
I hate New York in August
I hate Chicago in June
I hate Chicago in July
I hate Chicago in August

If a __wildcard__ is provided, then a new prompt will be produced for every value in the wildcard file. For example:

My favourite season is __seasons__

will produce:

My favourite season is Summer
My favourite season is August
My favourite season is Winter
My favourite season is Sprint

Usage

from dynamicprompts.generators import CombinatorialPromptGenerator

generator = CombinatorialPromptGenerator()
generator.generate("I love {red|green|blue} roses", max_prompts=5)

>> ['I love red roses', 'I love green roses', 'I love blue roses']

Notice that only 3 prompts were generated, even though we requested 5. Since there are only three options, i.e. red, green, and blue, only 3 unique prompts can be created. num_prompts in this case acts as an upper bound. Combinatorial generation can very quickly produce many more prompts than you intended. num_prompts is a safeguard to ensure that you don't accidentally produced thousands or tens of thousands of prompts.

Consider this template:

My favourite colours are __colours__, __colours__, and __colours__

If colours.txt contains 10 different colours, a combinatorial enumeration of that template will create 10 * 10 * 10 = 1000 different prompts. e.g.

My favourite colours are red, green, and blue
My favourite colours are red, green, and yellow
My favourite colours are red, green, and purple
My favourite colours are red, blue, and yellow
My favourite colours are red, blue, and purple
...

Magic Prompt

Using Gustavosta's MagicPrompt model, automatically generate new prompts from the input. Trained on 80,000 prompts from Lexica.art, it can help give you interesting new prompts on a given subject. Here are some automatically generated variations for "dogs playing football":

dogs playing football, in the streets of a japanese town at night, with people watching in wonder, in the style of studio ghibli and makoto shinkai, highly detailed digital art, trending on artstation
dogs playing football, in the background is a nuclear explosion. photorealism. hq. hyper. realistic. 4 k. award winning.
dogs playing football, in the background is a nuclear explosion. photorealistic. realism. 4 k wideshot. cinematic. unreal engine. artgerm. marc simonetti. jc leyendecker

This is compatible with the wildcard syntax described above.

Usage

from dynamicprompts.generators import RandomPromptGenerator
from dynamicprompts.generators.magicprompt import MagicPromptGenerator

generator = RandomPromptGenerator()
magic_generator = MagicPromptGenerator(
    generator,
    device=...,  # Torch device specifier (int, string, torch.device)
)

num_prompts = 5
generator.generate("I love {red|green|blue} roses", num_prompts)

>> ['I love red roses trending on artstation #vividart #pixiv', 'I love red roses trending on artstation artwork', 'I love blue roses breakfast club, cute, intricate, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, unreal engine 5, 8 k, art by artgerm and greg rutkowski and alphonse mucha', 'I love green roses I love green flowers, smile, Tristan Eaton, victo ngai, artgerm, RHADS, ross draws', 'I love red roses smile, Tristan Eaton, victo ngai, artgerm, RHADS, ross draws']

The first time you use it, the model is downloaded. It is approximately 500mb and so will take some time depending on how fast your connection is. It will also take a few seconds on first activation as the model is loaded into memory. Note, if you're low in VRAM, you might get a Cuda error. My GPU uses less than 8GB by YMMV.

Magic Prompt is not available by default, you need to install it as follows:

pip install "dynamicprompts[magicprompt]"

Other models

There a few alternatives to Gustavosta's model available. You can try:

magic_generator = MagicPromptGenerator(generator, "AUTOMATIC/promptgen-lexart")
magic_generator = MagicPromptGenerator(generator, "AUTOMATIC/promptgen-majinai-safe")
magic_generator = MagicPromptGenerator(generator, "AUTOMATIC/promptgen-majinai-unsafe")

You can find a longer list here Note that each model requires a download of large model files.

I'm feeling lucky

Use the lexica.art API to create random prompts. Useful if you're looking for inspiration, or are simply too lazy to think of your own prompts. When this option is selected, the template is used as a search string. For example, prompt "Mech warrior" might return:

A large robot stone statue in the middle of a forest by Greg Rutkowski, Sung Choi, Mitchell Mohrhauser, Maciej Kuciara, Johnson Ting, Maxim Verehin, Peter Konig, final fantasy , 8k photorealistic, cinematic lighting, HD, high details, atmospheric,

a beautiful portrait painting of a ( ( ( cyberpunk ) ) ) armor by simon stalenhag and pascal blanche and alphonse mucha and nekro. in style of digital art. colorful comic, film noirs, symmetry, brush stroke, vibrating colors, hyper detailed. octane render. trending on artstation

symmetry!! portrait of a robot astronaut, floral! horizon zero dawn machine, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha, 8 k

Usage

from dynamicprompts.generators import RandomPromptGenerator
from dynamicprompts.generators.feelinglucky import FeelingLuckyGenerator

generator = RandomPromptGenerator()
lucky_generator = FeelingLuckyGenerator(generator)

num_prompts = 5
lucky_generator.generate("I love {red|green|blue} roses", num_prompts)

>> ['“ guns and roses ” ', '🌹🥀🏜. 🌌🌠⭐. 💯. ', 'tattoo design, stencil, beautiful japanese girls face, roses and ivy surrounding by artgerm, artgerm, cat girl, anime ', 'rose made of glass dramatic lighting', 'a wireframe render of a red rose']

Attention Generator

If you are using Automatic1111 or a similar frontend to Stable Diffusion that uses attention syntax, e.g. (some text:1.4), AttentionGenerator will randomly add attention to various phrases in your prompt. This injects a small amount of randomness into your prompt.

Usage

from dynamicprompts.generators import RandomPromptGenerator
from dynamicprompts.generators.attentiongenerator import AttentionGenerator

generator = RandomPromptGenerator()
attention_generator = AttentionGenerator(generator)

prompt = "a portrait an anthropomorphic panda mage casting a spell, wearing mage robes, landscape in background, cute, dnd character art portrait, by jason felix and peter mohrbacher, cinematic lighting"
attention_generator.generate(prompt, num_prompts=1)

>> ['a portrait an anthropomorphic panda mage casting a spell, wearing (mage robes:0.77), landscape in background, cute, dnd character art portrait, by jason felix and peter mohrbacher, cinematic lighting']

You may get better results with AttentionGenerator by installing the spacy NLP library. Use this command to install it:

pip install spacy

When Spacy is available, an NLP model will automatically be downloaded on first use.

Jinja2 templates

If the standard template language is not sufficient for your needs, you can try the Jinja2 generator. Jinja2 templates have familiar programming constructs such as looping, conditionals, variables, etc. Youcan find a guide on using Jinja2 templates with Dynamic Prompts, here. Here is the minimal code you need to instantiate Jinja2 generator.

from dynamicprompts.generators import JinjaGenerator

generator = JinjaGenerator()
generator.generate("I love {red|green|blue} roses", num_images=5)

template = """
{% for colour in ['red', 'blue', 'green'] %}
    {% prompt %}I love {{ colour }} roses{% endprompt %}
{% endfor %}
"""

generator.generate(template)

>> ['I love red roses', 'I love blue roses', 'I love green roses']

Template syntax

You can find the complete syntax guide here

Syntax customisation

To address potential syntax clashes with other tools it is possible to change various tokens. Instead of {red|green|blue} you can configure the library to use the < > pair instead, e.g. <red|green|blue>. You can also change the __ used in wildcards. So instead of __colours__, you can configure wildcards to use **, e.g. **colours**

from dynamicprompts.generators import RandomPromptGenerator
from dynamicprompts.parser.config import ParserConfig

parser_config = ParserConfig(variant_start="<", variant_end=">", wildcard_wrap="**")
generator = RandomPromptGenerator(parser_config=parser_config)

Wildcard Collections

You can bootstrap your wildcard library by using our pre-existing collections. You'll find just under 80,000 wildcards divided into 1900 files. Feel free to pick and choose or take them in their entirety.

Dynamic Prompts in the wild.

Dynamic Prompts has been used in:

  1. SD Dynamic Prompts Auto1111 extension
  2. Deforum 0.7 colab
  3. ComfyUI Nodes

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Templating language for generating prompts for text to image generators such as Stable Diffusion

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