stable-diffusion-api
Version:
API translation for Automatic1111 Stable Diffusion WebUI
180 lines (128 loc) • 5.85 kB
Markdown
[](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](
- [Requisites](
- [Installation](
- [Usage](
- [Instantiation](
- [Authentication](
- [txt2img](
- [img2img](
- [ControlNet Extension API usage](
- [Get models and modules](
- [ControlNetUnit](
- [detect](
- 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.
```bash
npm install stable-diffusion-api
```
```bash
yarn add stable-diffusion-api
```
```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",
});
```
Use the `--api-auth` command line argument with "username:password" on the server to enable API authentication.
```typescript
api.setAuth("username", "password");
```
```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
|:-------------------------:
| 
```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 |
| :-------------------------------: | :----------------------------: |
|  |  |
---
- 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.
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();
```
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 |
| :----------------------------------: | :------------------------------------: | :---------------------------------------: |
|  |  |  |
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 |
| :--------------------------: | :--------------------------------: |
|  |  |