92 lines
3.1 KiB
Markdown
92 lines
3.1 KiB
Markdown
# text2prompt
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This is an extension to make prompt from simple text for [Stable Diffusion web UI by AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
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Currently, only prompts consisting of some danbooru tags can be generated.
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## Installation
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### Extensions tab on WebUI
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Copy `https://github.com/toshiaki1729/stable-diffusion-webui-text2prompt.git` into "Install from URL" tab and "Install".
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### Install Manually
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To install, clone the repository into the `extensions` directory and restart the web UI.
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On the web UI directory, run the following command to install:
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```commandline
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git clone https://github.com/toshiaki1729/stable-diffusion-webui-text2prompt.git extensions/text2prompt
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```
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## Usage
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1. Type some words into "Input Theme"
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1. Push "Generate" button
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### Tips
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- For more creative result
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- increase "k value" or "p value"
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- disable "Use weighted choice"
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- use "Cutoff and Power" and decrease "Power"
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- or use "Softmax" (may generate unwanted tags more often)
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- For more strict result
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- decrease "k value" or "p value"
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- use "Cutoff and Power" and increase "Power"
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- You can enter very long sentences, but the more specific it is, the fewer results you will get.
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## How it works
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It's doing nothing special;
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1. Danbooru tags and it's descriptions are in the `data` folder
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- descriptions are generated from wiki and already tokenized
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- [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) model is used to tokenize the text
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- for now, some tags (such as <1k tagged or containing title of the work) are deleted to prevent from "noisy" result
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1. Tokenize your input text and calculate cosine similarity to each tag descriptions
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1. Choose some tags depending on their similarities
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---
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### More detailed
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$i \in N = \\{1, 2, ..., n\\}$ for index number of the tag
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$s_i = S_C(d_i, t)$ for cosine similarity between tag description $d_i$ and your text $t$
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$P_i$ for probability for the tag to be chosen
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### "Method to convert similarity into probability"
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#### "Cutoff and Power"
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$$p_i = \text{clamp}(s_i, 0, 1)^{\text{Power}} = \text{max}(s_i, 0)^{\text{Power}}$$
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#### "Softmax"
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$$p_i = \sigma(\\{s_n|n \in N\\})_i = \dfrac{e^{s_i}}{ \Sigma_{j \in N}\ e^{s_j} }$$
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### "Sampling method"
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Yes, it doesn't sample like other "true" language models do, so "Filtering method" might be better.
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#### "NONE"
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$$P_i = p_i$$
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#### "Top-k"
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$$
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P_i = \begin{cases}
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\dfrac{p_i}{\Sigma p_j \text{ for all top-}k} & \text{if } p_i \text{ is top-}k \text{ largest in } \\{p_n | n \in N \\} \\
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0 & \text{otherwise} \\
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\end{cases}
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$$
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#### "Top-p (Nucleus)"
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- Find smallest $N_p \subset N$ such that $\Sigma_{i \in N_p}\ p_i\ \geq p$
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- set $N_p=\emptyset$ at first, and add index of $p_{(k)}$ into $N_p$ where $p_{(k)}$ is the $k$-th largest in $\\{p_n | n \in N \\}$ for $k = 1, 2, ..., n$, until the equation holds.
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$$
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P_i = \begin{cases}
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\dfrac{p_i}{\Sigma p_j \text{ for all }j \in N_p} & \text{if } i \in N_p \\
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0 & \text{otherwise} \\
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\end{cases}
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$$
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Finally, the tags will be chosen randomly while the number $\leq$ "Max number of tags".
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