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https://github.com/invoke-ai/InvokeAI
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214 lines
11 KiB
Markdown
214 lines
11 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 to run, one line per
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prompt. The text file must be composed with a text editor (e.g. Notepad) and not a word processor.
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Each line should look like what you 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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(invokeai) ~/stable-diffusion$ python3 scripts/invoke.py --from_file "path/to/prompts.txt"
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```
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You may read a series of prompts from standard input by providing a filename of `-`:
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```bash
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(invokeai) ~/stable-diffusion$ echo "a beautiful day" | python3 scripts/invoke.py --from_file -
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```
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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
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Diffusion to 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 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 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" -s 20 -W 512 -H 768 -C 7.5 -A k_euler_a -S 1654590180`
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<div align="center" markdown>
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</div>
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That image has a woman, so if we want the horse without a rider, we can influence the image not to have a woman by putting [woman] in the prompt, like 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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<div align="center" markdown>
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</div>
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That's nice - but say we also don't want the image to be quite so blue. We can 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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<div align="center" markdown>
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</div>
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Getting close - but there's no sense in having a saddle when our horse doesn't 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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<div align="center" markdown>
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</div>
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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` (`1`=default), to decrease or increase "attention" (= a mix of per-token CFG weighting multiplier and, for `-`, a weighted blend with the prompt without the term).
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The following will be 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: `a tall thin man picking (apricots)+` `a tall thin man picking (apricots)-` `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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* all of the above with explicit numbers `a tall thin man picking (apricots)1.1` `a tall thin man (picking (apricots)1.3)1.1`. (`+` is equivalent to 1.1, `++` is pow(1.1,2), `+++` is pow(1.1,3), etc; `-` means 0.9, `--` means pow(0.9,2), etc.)
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* attention also applies to `[unconditioning]` so `a tall thin man picking apricots [(ladder)0.01]` will *very gently* nudge SD away from trying to draw the man on a ladder
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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 - `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,1)` is equivalent to `a tall thin man picking apricots:1 a tall thin man picking pears:1` in the 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 argument to the blend weights, eg `("a tall thin man picking apricots", "a tall thin man picking pears").blend(1,-1,no_normalize)`. very fun to explore local maxima in the feature space, but also easy to produce garbage output.
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See the section below on "Prompt Blending" for more information about how this works.
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### Cross-Attention Control ('prompt2prompt')
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Denoise with a given prompt and then re-use the attention→pixel maps to substitute words in the original prompt for words in a new prompt. Based off [bloc97's colab](https://github.com/bloc97/CrossAttentionControl).
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* `a ("fluffy cat").swap("smiling dog") eating a hotdog`.
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* quotes optional: `a (fluffy cat).swap(smiling dog) eating a hotdog`.
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* for single word substitutions parentheses are also optional: `a cat.swap(dog) eating a hotdog`.
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* Supports options `s_start`, `s_end`, `t_start`, `t_end` (each 0-1) loosely corresponding to bloc97's `prompt_edit_spatial_start/_end` and `prompt_edit_tokens_start/_end` but with the math swapped to make it easier to intuitively understand.
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* Example usage:`a (cat).swap(dog, s_end=0.3) eating a hotdog` - the `s_end` argument means that the "spatial" (self-attention) edit will stop having any effect after 30% (=0.3) of the steps have been done, leaving Stable Diffusion with 70% of the steps where it is free to decide for itself how to reshape the cat-form into a dog form.
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* The numbers represent a percentage through the step sequence where the edits should happen. 0 means the start (noisy starting image), 1 is the end (final image).
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* For img2img, the step sequence does not start at 0 but instead at (1-strength) - so if strength is 0.7, s_start and s_end must both be greater than 0.3 (1-0.7) to have any effect.
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* Convenience option `shape_freedom` (0-1) to specify how much "freedom" Stable Diffusion should have to change the shape of the subject being swapped.
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* `a (cat).swap(dog, shape_freedom=0.5) eating a hotdog`.
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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 syntax, you will need to "escape" these using a backslash, so that`(my_keyword)` becomes `\(my_keyword\)`. Otherwise, the prompt parser will attempt to interpret the parentheses as part of the prompt syntax and it will get confused.
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## **Prompt Blending**
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You may blend together different sections of the prompt to explore the
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AI's latent semantic space and generate interesting (and often
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surprising!) 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
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sphere with 75% of the concept of a red cube. The blend weights can
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use any combination of integers and floating point numbers, and they
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do not need to add up to 1. Everything to the left of the `:XX` up to
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the previous `:XX` is used for 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
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two concepts may not match yours, leading to surprising effects. To
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illustrate, here are three images generated using various combinations
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of blend weights. As usual, unless you fix the seed, the prompts will give you
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different results each time you run them.
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---
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<div align="center" markdown>
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### "blue sphere, red cube, hybrid"
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</div>
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This example doesn't use melding at all and represents the default way
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of mixing concepts.
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<div align="center" markdown>
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</div>
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It's interesting to see how the AI expressed the concept of "cube" as
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the four quadrants of the enclosing frame. If you look closely, there
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is depth there, so the enclosing frame is actually a cube.
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<div align="center" markdown>
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### "blue sphere:0.25 red cube:0.75 hybrid"
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</div>
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Now that's interesting. We get neither a blue sphere nor a red cube,
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but a red sphere embedded in a brick wall, which represents a melding
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of concepts within the AI's "latent space" of semantic
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representations. Where is Ludwig Wittgenstein when you need him?
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<div align="center" markdown>
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### "blue sphere:0.75 red cube:0.25 hybrid"
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</div>
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Definitely more blue-spherey. The cube is gone entirely, but it's
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really cool abstract art.
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<div align="center" markdown>
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### "blue sphere:0.5 red cube:0.5 hybrid"
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</div>
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Whoa...! I see blue and red, but no spheres or cubes. Is the word
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"hybrid" summoning up the concept of some sort of scifi creature?
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Let's find out.
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<div align="center" markdown>
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### "blue sphere:0.5 red cube:0.5"
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</div>
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Indeed, removing the word "hybrid" produces an image that is more like
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what we'd expect.
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In conclusion, prompt blending is great for exploring creative space,
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but can be difficult to direct. A forthcoming release of InvokeAI will
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feature more deterministic prompt weighting.
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