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https://github.com/invoke-ai/InvokeAI
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380 lines
19 KiB
Markdown
380 lines
19 KiB
Markdown
---
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title: Prompting-Features
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---
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# :octicons-command-palette-24: Prompting-Features
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## **Reading Prompts from a File**
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You can automate `invoke.py` by providing a text file with the prompts you want
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to run, one line per prompt. The text file must be composed with a text editor
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(e.g. Notepad) and not a word processor. Each line should look like what you
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would type at the invoke> prompt:
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```bash
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"a beautiful sunny day in the park, children playing" -n4 -C10
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"stormy weather on a mountain top, goats grazing" -s100
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"innovative packaging for a squid's dinner" -S137038382
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```
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Then pass this file's name to `invoke.py` when you invoke it:
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```bash
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python scripts/invoke.py --from_file "/path/to/prompts.txt"
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```
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You may also read a series of prompts from standard input by providing
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a filename of `-`. For example, here is a python script that creates a
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matrix of prompts, each one varying slightly:
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```bash
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#!/usr/bin/env python
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adjectives = ['sunny','rainy','overcast']
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samplers = ['k_lms','k_euler_a','k_heun']
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cfg = [7.5, 9, 11]
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for adj in adjectives:
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for samp in samplers:
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for cg in cfg:
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print(f'a {adj} day -A{samp} -C{cg}')
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```
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Its output looks like this (abbreviated):
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```bash
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a sunny day -Aklms -C7.5
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a sunny day -Aklms -C9
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a sunny day -Aklms -C11
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a sunny day -Ak_euler_a -C7.5
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a sunny day -Ak_euler_a -C9
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...
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a overcast day -Ak_heun -C9
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a overcast day -Ak_heun -C11
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```
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To feed it to invoke.py, pass the filename of "-"
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```bash
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python matrix.py | python scripts/invoke.py --from_file -
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```
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When the script is finished, each of the 27 combinations
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of adjective, sampler and CFG will be executed.
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The command-line interface provides `!fetch` and `!replay` commands
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which allow you to read the prompts from a single previously-generated
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image or a whole directory of them, write the prompts to a file, and
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then replay them. Or you can create your own file of prompts and feed
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them to the command-line client from within an interactive session.
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See [Command-Line Interface](CLI.md) for details.
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---
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## **Negative and Unconditioned Prompts**
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Any words between a pair of square brackets will instruct Stable Diffusion to
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attempt to ban the concept from the generated image.
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```text
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this is a test prompt [not really] to make you understand [cool] how this works.
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```
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In the above statement, the words 'not really cool` will be ignored by Stable
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Diffusion.
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Here's a prompt that depicts what it does.
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original prompt:
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`#!bash "A fantastical translucent pony made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
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<figure markdown>
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</figure>
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That image has a woman, so if we want the horse without a rider, we can
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influence the image not to have a woman by putting [woman] in the prompt, like
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this:
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`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
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<figure markdown>
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</figure>
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That's nice - but say we also don't want the image to be quite so blue. We can
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add "blue" to the list of negative prompts, so it's now [woman blue]:
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`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
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<figure markdown>
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</figure>
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Getting close - but there's no sense in having a saddle when our horse doesn't
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have a rider, so we'll add one more negative prompt: [woman blue saddle].
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`#!bash "A fantastical translucent poney made of water and foam, ethereal, radiant, hyperalism, scottish folklore, digital painting, artstation, concept art, smooth, 8 k frostbite 3 engine, ultra detailed, art by artgerm and greg rutkowski and magali villeneuve [woman blue saddle]" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
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<figure markdown>
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</figure>
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!!! notes "Notes about this feature:"
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* The only requirement for words to be ignored is that they are in between a pair of square brackets.
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* You can provide multiple words within the same bracket.
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* You can provide multiple brackets with multiple words in different places of your prompt. That works just fine.
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* To improve typical anatomy problems, you can add negative prompts like `[bad anatomy, extra legs, extra arms, extra fingers, poorly drawn hands, poorly drawn feet, disfigured, out of frame, tiling, bad art, deformed, mutated]`.
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---
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## **Prompt Syntax Features**
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The InvokeAI prompting language has the following features:
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### Attention weighting
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Append a word or phrase with `-` or `+`, or a weight between `0` and `2`
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(`1`=default), to decrease or increase "attention" (= a mix of per-token CFG
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weighting multiplier and, for `-`, a weighted blend with the prompt without the
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term).
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The following syntax is recognised:
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- single words without parentheses: `a tall thin man picking apricots+`
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- single or multiple words with parentheses:
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`a tall thin man picking (apricots)+` `a tall thin man picking (apricots)-`
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`a tall thin man (picking apricots)+` `a tall thin man (picking apricots)-`
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- more effect with more symbols `a tall thin man (picking apricots)++`
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- nesting `a tall thin man (picking apricots+)++` (`apricots` effectively gets
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`+++`)
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- all of the above with explicit numbers `a tall thin man picking (apricots)1.1`
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`a tall thin man (picking (apricots)1.3)1.1`. (`+` is equivalent to 1.1, `++`
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is pow(1.1,2), `+++` is pow(1.1,3), etc; `-` means 0.9, `--` means pow(0.9,2),
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etc.)
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- attention also applies to `[unconditioning]` so
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`a tall thin man picking apricots [(ladder)0.01]` will _very gently_ nudge SD
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away from trying to draw the man on a ladder
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You can use this to increase or decrease the amount of something. Starting from
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this prompt of `a man picking apricots from a tree`, let's see what happens if
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we increase and decrease how much attention we want Stable Diffusion to pay to
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the word `apricots`:
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<figure markdown>
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</figure>
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Using `-` to reduce apricot-ness:
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| `a man picking apricots- from a tree` | `a man picking apricots-- from a tree` | `a man picking apricots--- from a tree` |
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| ------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
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Using `+` to increase apricot-ness:
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| `a man picking apricots+ from a tree` | `a man picking apricots++ from a tree` | `a man picking apricots+++ from a tree` | `a man picking apricots++++ from a tree` | `a man picking apricots+++++ from a tree` |
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| ------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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You can also change the balance between different parts of a prompt. For
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example, below is a `mountain man`:
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<figure markdown>
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</figure>
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And here he is with more mountain:
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| `mountain+ man` | `mountain++ man` | `mountain+++ man` |
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| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
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Or, alternatively, with more man:
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| `mountain man+` | `mountain man++` | `mountain man+++` | `mountain man++++` |
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| ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- | ---------------------------------------------- |
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### Blending between prompts
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- `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
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- The existing prompt blending using `:<weight>` will continue to be supported -
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`("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)`
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is equivalent to
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`a tall thin man picking apricots:1 a tall thin man picking pears:1` in the
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old syntax.
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- Attention weights can be nested inside blends.
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- Non-normalized blends are supported by passing `no_normalize` as an additional
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argument to the blend weights, eg
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`("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,-1,no_normalize)`.
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very fun to explore local maxima in the feature space, but also easy to
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produce garbage output.
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See the section below on "Prompt Blending" for more information about how this
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works.
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### Cross-Attention Control ('prompt2prompt')
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Sometimes an image you generate is almost right, and you just want to change one
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detail without affecting the rest. You could use a photo editor and inpainting
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to overpaint the area, but that's a pain. Here's where `prompt2prompt` comes in
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handy.
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Generate an image with a given prompt, record the seed of the image, and then
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use the `prompt2prompt` syntax to substitute words in the original prompt for
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words in a new prompt. This works for `img2img` as well.
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For example, consider the prompt `a cat.swap(dog) playing with a ball in the forest`. Normally, because of the word words interact with each other when doing a stable diffusion image generation, these two prompts would generate different compositions:
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- `a cat playing with a ball in the forest`
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- `a dog playing with a ball in the forest`
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| `a cat playing with a ball in the forest` | `a dog playing with a ball in the forest` |
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| --- | --- |
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| img | img |
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- For multiple word swaps, use parentheses: `a (fluffy cat).swap(barking dog) playing with a ball in the forest`.
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- To swap a comma, use quotes: `a ("fluffy, grey cat").swap("big, barking dog") playing with a ball in the forest`.
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- Supports options `t_start` and `t_end` (each 0-1) loosely corresponding to bloc97's `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to
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intuitively understand. `t_start` and `t_end` are used to control on which steps cross-attention control should run. With the default values `t_start=0` and `t_end=1`, cross-attention control is active on every step of image generation. Other values can be used to turn cross-attention control off for part of the image generation process.
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- For example, if doing a diffusion with 10 steps for the prompt is `a cat.swap(dog, t_start=0.3, t_end=1.0) playing with a ball in the forest`, the first 3 steps will be run as `a cat playing with a ball in the forest`, while the last 7 steps will run as `a dog playing with a ball in the forest`, but the pixels that represent `dog` will be locked to the pixels that would have represented `cat` if the `cat` prompt had been used instead.
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- Conversely, for `a cat.swap(dog, t_start=0, t_end=0.7) playing with a ball in the forest`, the first 7 steps will run as `a dog playing with a ball in the forest` with the pixels that represent `dog` locked to the same pixels that would have represented `cat` if the `cat` prompt was being used instead. The final 3 steps will just run `a cat playing with a ball in the forest`.
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> For img2img, the step sequence does not start at 0 but instead at `(1.0-strength)` - so if the img2img `strength` is `0.7`, `t_start` and `t_end` must both be greater than `0.3` (`1.0-0.7`) to have any effect.
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Prompt2prompt `.swap()` is not compatible with xformers, which will be temporarily disabled when doing a `.swap()` - so you should expect to use more VRAM and run slower that with xformers enabled.
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The `prompt2prompt` code is based off
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[bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
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Note that `prompt2prompt` is not currently working with the runwayML inpainting
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model, and may never work due to the way this model is set up. If you attempt to
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use `prompt2prompt` you will get the original image back. However, since this
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model is so good at inpainting, a good substitute is to use the `clipseg` text
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masking option:
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```bash
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invoke> a fluffy cat eating a hotdog
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Outputs:
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[1010] outputs/000025.2182095108.png: a fluffy cat eating a hotdog
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invoke> a smiling dog eating a hotdog -I 000025.2182095108.png -tm cat
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```
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### Escaping parantheses () and speech marks ""
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If the model you are using has parentheses () or speech marks "" as part of its
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syntax, you will need to "escape" these using a backslash, so that`(my_keyword)`
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becomes `\(my_keyword\)`. Otherwise, the prompt parser will attempt to interpret
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the parentheses as part of the prompt syntax and it will get confused.
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---
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## **Prompt Blending**
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You may blend together different sections of the prompt to explore the AI's
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latent semantic space and generate interesting (and often surprising!)
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variations. The syntax is:
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```bash
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blue sphere:0.25 red cube:0.75 hybrid
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```
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This will tell the sampler to blend 25% of the concept of a blue sphere with 75%
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of the concept of a red cube. The blend weights can use any combination of
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integers and floating point numbers, and they do not need to add up to 1.
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Everything to the left of the `:XX` up to the previous `:XX` is used for
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merging, so the overall effect is:
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```bash
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0.25 * "blue sphere" + 0.75 * "white duck" + hybrid
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```
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Because you are exploring the "mind" of the AI, the AI's way of mixing two
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concepts may not match yours, leading to surprising effects. To illustrate, here
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are three images generated using various combinations of blend weights. As
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usual, unless you fix the seed, the prompts will give you different results each
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time you run them.
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<figure markdown>
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### "blue sphere, red cube, hybrid"
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</figure>
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This example doesn't use melding at all and represents the default way of mixing
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concepts.
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<figure markdown>
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</figure>
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It's interesting to see how the AI expressed the concept of "cube" as the four
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quadrants of the enclosing frame. If you look closely, there is depth there, so
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the enclosing frame is actually a cube.
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<figure markdown>
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### "blue sphere:0.25 red cube:0.75 hybrid"
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</figure>
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Now that's interesting. We get neither a blue sphere nor a red cube, but a red
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sphere embedded in a brick wall, which represents a melding of concepts within
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the AI's "latent space" of semantic representations. Where is Ludwig
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Wittgenstein when you need him?
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<figure markdown>
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### "blue sphere:0.75 red cube:0.25 hybrid"
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</figure>
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Definitely more blue-spherey. The cube is gone entirely, but it's really cool
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abstract art.
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<figure markdown>
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### "blue sphere:0.5 red cube:0.5 hybrid"
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</figure>
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Whoa...! I see blue and red, but no spheres or cubes. Is the word "hybrid"
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summoning up the concept of some sort of scifi creature? Let's find out.
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<figure markdown>
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### "blue sphere:0.5 red cube:0.5"
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</figure>
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Indeed, removing the word "hybrid" produces an image that is more like what we'd
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expect.
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In conclusion, prompt blending is great for exploring creative space, but can be
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difficult to direct. A forthcoming release of InvokeAI will feature more
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deterministic prompt weighting.
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