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https://github.com/jaschahuisman/sd-api
A Typescript API client for AUTOMATIC111/stable-diffusion-webui
https://github.com/jaschahuisman/sd-api
api automatic1111 client javascript stable-diffusion stable-diffusion-webui typescript
Last synced: 8 days ago
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A Typescript API client for AUTOMATIC111/stable-diffusion-webui
- Host: GitHub
- URL: https://github.com/jaschahuisman/sd-api
- Owner: jaschahuisman
- License: mit
- Created: 2023-04-01T17:43:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-17T08:31:53.000Z (about 1 year ago)
- Last Synced: 2024-12-11T07:38:41.441Z (16 days ago)
- Topics: api, automatic1111, client, javascript, stable-diffusion, stable-diffusion-webui, typescript
- Language: TypeScript
- Homepage: https://www.npmjs.com/package/stable-diffusion-api
- Size: 1.42 MB
- Stars: 97
- Watchers: 4
- Forks: 18
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Stable Diffusion Api
[](https://www.npmjs.com/package/stable-diffusion-api)
[](https://www.github.com/jaschahuisman/sd-api)
A Typescript API client for [AUTOMATIC111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) API that is unremarkably inspired by the Python library [webuiapi](https://github.com/mix1009/sdwebuiapi).
- [Stable Diffusion Api](#stable-diffusion-api)
- [Requisites](#requisites)
- [Installation](#installation)
- [Usage](#usage)
- [Instantiation](#instantiation)
- [Authentication](#authentication)
- [txt2img](#txt2img)
- [img2img](#img2img)
- [ControlNet Extension API usage](#controlnet-extension-api-usage)
- [Get models and modules](#get-models-and-modules)
- [ControlNetUnit](#controlnetunit)
- [detect](#detect)
## Requisites
- To use this API client, you have to run `stable-diffusion-webui` with the `--api` command line argument.
- Optionally you can add `--nowebui` to disable the web interface.
## Installation
```bash
npm install stable-diffusion-api
```
```bash
yarn add stable-diffusion-api
```
## Usage
### Instantiation
```typescript
import StableDiffusionApi from "stable-diffusion-api";
const api = new StableDiffusionApi();
const api = new StableDiffusionApi({
host: "localhost",
port: 7860,
protocol: "http",
defaultSampler: "Euler a",
defaultStepCount: 20,
});
const api = new StableDiffusionApi({
baseUrl: "http://localhost:7860",
});
```
### Authentication
Use the `--api-auth` command line argument with "username:password" on the server to enable API authentication.
```typescript
api.setAuth("username", "password");
```
### txt2img
```typescript
const result = await api.txt2img({
prompt: "An AI-powered robot that accidentally starts doing everyone's job, causing chaos in the workplace."
...
})
result.image.toFile('result.png')
```
| Result
|:-------------------------:
| 
### img2img
```typescript
const image = sharp('image.png')
const result = await api.img2img({
init_images: [image],
prompt: "Man, scared of AGI, running away on a burning lava floor."
...
})
result.image.toFile('result.png')
```
| Input | Result |
| :-------------------------------: | :----------------------------: |
|  |  |
---
## ControlNet Extension API usage
- To use the ControlNet API, you must have installed the [ControlNet extension](https://github.com/Mikubill/sd-webui-controlnet) into your `stable-diffusion-webui` instance.
- It's also necessary to have the desired ControlNet models installed into the extension's models directory.
### Get models and modules
To get a list of all installed ControlNet models and modules, you can use the `api.ControlNet.getModels()` and `api.ControlNet.getModules()` methods.
```typescript
const models = await api.ControlNet.getModels();
const modules = await api.ControlNet.getModules();
```
### ControlNetUnit
To make use of the ControlNet API, you must first instantiate a `ControlNetUnit` object in wich you can specify the ControlNet model and preprocessor to use. Next, to use the unit, you must pass it as an array in the `controlnet_units` argument in the `txt2img` or `img2img` methods.
It's also possible to use multiple ControlNet units in the same request. To get some good results, it's recommended to use lower weights for each unit by setting the `weight` argument to a lower value.
To get a list of all installed ControlNet models, you can use the `api.ControlNet.getModels()` method.
```typescript
const image = sharp("image.png");
const controlNetUnit = new ControlNetUnit({
model: "control_sd15_depth [fef5e48e]",
module: "depth",
input_images: [image],
processor_res: 512,
threshold_a: 64,
threshold_b: 64,
});
const result = await api.txt2img({
prompt:
"Young lad laughing at all artists putting hard work and effort into their work.",
controlnet_units: [controlNetUnit],
});
result.image.toFile("result.png");
// To access the preprocessing result, you can use the following:
const depth = result.images[1];
depth.toFile("depth.png");
```
| Input | Result | Depth |
| :----------------------------------: | :------------------------------------: | :---------------------------------------: |
|  |  |  |
### detect
Uses the selected ControlNet proprocessor module to predict a detection on the input image. To make use of the detection result, you must use the model of choise in the `txt2img` or `img2img` without a preprocessor enabled (use `"none"` as the preprocessor module).
This comes in handy when you just want a detection result without generating a whole new image.
```typescript
const image = sharp("image.png");
const result = await api.ControlNet.detect({
controlnet_module: "depth",
controlnet_input_images: [image],
controlnet_processor_res: 512,
controlnet_threshold_a: 64,
controlnet_threshold_b: 64,
});
result.image.toFile("result.png");
```
| Input | Result |
| :--------------------------: | :--------------------------------: |
|  |  |