76 lines
2.5 KiB
Markdown
76 lines
2.5 KiB
Markdown
# text2prompt
|
|
|
|
|
|
This is an extension to make prompt from simple text for [Stable Diffusion web UI by AUTOMATIC1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
|
|
Currently, only prompts consisting of some danbooru tags can be generated.
|
|
|
|
## Installation
|
|
### Extensions tab on WebUI
|
|
Copy `https://github.com/toshiaki1729/stable-diffusion-webui-text2prompt.git` into "Install from URL" tab and "Install".
|
|
|
|
### Install Manually
|
|
|
|
To install, clone the repository into the `extensions` directory and restart the web UI.
|
|
On the web UI directory, run the following command to install:
|
|
```commandline
|
|
git clone https://github.com/toshiaki1729/stable-diffusion-webui-text2prompt.git extensions/text2prompt
|
|
```
|
|
|
|
|
|
## Usage
|
|
1. Type some words into "Input Theme"
|
|
1. Push "Generate" button
|
|
|
|
|
|
## How it works
|
|
It's doing nothing special;
|
|
|
|
1. Danbooru tags and it's descriptions are in the `data` folder
|
|
- descriptions are generated from wiki and already tokenised
|
|
- [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) model is used to tokenize the text
|
|
- for now, some tags (<1k tagged, containing title of the work) are deleted to prevent from "noisy" result
|
|
1. Tokenize your input text and calculate cosine similarity with all tag descriptions
|
|
1. Choose some tags depending on their similarities
|
|
|
|
|
|
|
|
---
|
|
|
|
### More detailed
|
|
$i \in N = \\{1, 2, ..., n\\}$ for index number of the tag
|
|
cosine similarity between tag description $d_i$ and your text $t$ : $S_C(d_i, t) = s_i$
|
|
probablity for the tag to be chosen : $P_i$
|
|
|
|
### "Method to convert similarity into probablity"
|
|
#### "Cutoff and Power"
|
|
- $p_i = \text{clamp}(s_i, 0, 1)^{\text{Power}} = \text{max}(s_i, 0)^{\text{Power}}$
|
|
#### "Softmax"
|
|
- $p_i = \text{softmax}(s_i)$
|
|
|
|
### "Sampling method"
|
|
#### "NONE"
|
|
|
|
$$P_i = p_i$$
|
|
|
|
#### "Top-k"
|
|
|
|
$$
|
|
P_i = \begin{cases}
|
|
\frac{p_i}{\Sigma p_j \text{ for all top-}k} & \text{if } p_i \text{ is top-}k \text{ largest in } \\{p_n | n \in N \\} \\
|
|
0 & \text{otherwise} \\
|
|
\end{cases}
|
|
$$
|
|
|
|
#### "Top-p (Nucleus)"
|
|
- $N_p \subset N$ such that $\Sigma_{i \in N_p}\ p_i\ \geq p$
|
|
- set $N_p=\emptyset$ at first, and add $k$ into $N_p$ where $p_k$ is the $k$-th largest in $\\{p_n | n \in N \\}$, while the equation holds.
|
|
|
|
$$
|
|
P_i = \begin{cases}
|
|
\frac{p_i}{\Sigma p_j \text{ for all }j \in N_p} & \text{if } i \in N_p \\
|
|
0 & \text{otherwise} \\
|
|
\end{cases}
|
|
$$
|
|
|
|
Finally, the tags will be chosen. The number of the tags will be $\leq$ "Max number of tags".
|