> For the complete documentation index, see [llms.txt](https://docs.myshell.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.myshell.ai/ko/create/pro-config-mode/api-reference/widgets/56-controlnet-with-civitai.md).

# ControlNet with Civitai

{% hint style="info" %}
This widget supports multiple actions. For a more comprehensive understanding of its functionality, we recommend reviewing the following documentation carefully.

You need to pass both the `action` and other input parameters of the chosen action to your `module_config`
{% endhint %}

## Try it in the Widget Center

Click this [url](https://app.myshell.ai/robot-workshop/widget/1793089562715484160) to try this widget and copy the Pro Config template.

## Usage

### Generate Picture with ControlNet

<mark style="color:green;">`action`</mark> `txt2img`

**Input Parameters**

<table><thead><tr><th>Name</th><th>Type</th><th>Description</th><th>Default</th><th data-type="checkbox">Required</th></tr></thead><tbody><tr><td>action</td><td><code>string</code></td><td>The action of ControlNet, txt2img or img2img</td><td>txt2img</td><td>true</td></tr><tr><td>model</td><td><code>string</code></td><td>The model id from civitai (SD1.5, SDXL 1.0, PlaygroundV2). How to get it? Click on a model page on civitai, and copy the series number within the download link.</td><td>64094</td><td>true</td></tr><tr><td>controlnet_model</td><td><code>string</code></td><td>The ControlNet model id from civitai. How to get it? Click on a model page on civitai, and copy the series number within the download link.</td><td>10971</td><td>true</td></tr><tr><td>image</td><td><code>string</code></td><td>The input image, can be a url or base64 sting</td><td></td><td>true</td></tr><tr><td>no_mask</td><td><code>boolean</code></td><td>Whether to use mask</td><td>False</td><td>true</td></tr><tr><td>mask</td><td><code>string</code></td><td>The correspond mask, can be a url or base64 sting. 1 for mask region</td><td></td><td>false</td></tr><tr><td>prompt</td><td><code>string</code></td><td>The text prompt for ControlNet. Add lora? add `` to your prompt. `$id` is the series number and `$weight` is the lora weight you want (always set to 1.0). You can use multiple loras.</td><td></td><td>true</td></tr><tr><td>negative_prompt</td><td><code>string</code></td><td>The negative prompt for ControlNet.</td><td>(worst quality, low quality:1.4),(malformed hands:1.4),(poorly drawn hands:1.4),(mutated fingers:1.4),(extra limbs:1.35),(poorly drawn face:1.4),bad leg,strange leg, poor eyes, full screen of face</td><td>true</td></tr><tr><td>controlnet_module</td><td><code>string</code></td><td>The ControNet module</td><td>none</td><td>true</td></tr><tr><td>guidance_start</td><td><code>number</code></td><td>ControlNet guidance start</td><td>0</td><td>true</td></tr><tr><td>guidance_end</td><td><code>number</code></td><td>ControlNet guidance end</td><td>1</td><td>true</td></tr><tr><td>control_mode</td><td><code>string</code></td><td>The improved guess mode</td><td>Balanced</td><td>true</td></tr><tr><td>weight</td><td><code>number</code></td><td>The weight of the controlnet model</td><td>1</td><td>true</td></tr><tr><td>resize_mode</td><td><code>string</code></td><td>Four modes for output shape calculation: (1) Keep: keep original shape, (2) Certain: based on input width/length (divisible by 32), (3,4) min/max ratio: keep aspect ratio, the resize factor is the min/max of (h/H, w/W).</td><td>certain</td><td>true</td></tr><tr><td>threshold_a</td><td><code>integer</code></td><td>The threshold A for controlnet model</td><td>64</td><td>true</td></tr><tr><td>threshold_b</td><td><code>integer</code></td><td>The threshold B for controlnet model</td><td>64</td><td>true</td></tr><tr><td>steps</td><td><code>integer</code></td><td>Steps for sampler to step whle sampling</td><td>25</td><td>true</td></tr><tr><td>cfg_scale</td><td><code>number</code></td><td>Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results. Default to 7.</td><td>7.0</td><td>true</td></tr><tr><td>sampler</td><td><code>string</code></td><td>Sampler for diffusion model inference</td><td>DPM++ 2M</td><td>true</td></tr><tr><td>height</td><td><code>integer</code></td><td>Height of the generated images</td><td>512</td><td>true</td></tr><tr><td>width</td><td><code>integer</code></td><td>Width of the generated images</td><td>512</td><td>true</td></tr><tr><td>seed</td><td><code>integer</code></td><td>Random seed for generation process. -1 means random seed</td><td>-1</td><td>false</td></tr><tr><td>clip_skip</td><td><code>integer</code></td><td>Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.</td><td>1</td><td>true</td></tr></tbody></table>

**Output Parameters**

| Name | Type     | Description                                                                                                  | File Type |
| ---- | -------- | ------------------------------------------------------------------------------------------------------------ | --------- |
| url  | `string` | The url of generated image, stored in the cloud. Only temporarily effective, will be cleared in a few hours. | `image`   |

**Output Example**

{% tabs %}
{% tab title="success" %}
{% code fullWidth="false" %}

```json
{
  "url": "https://image.myshell.ai/image/chat/embed_obj/7758545/202405220201/6ee8737899794e9bb2e12b5844a4b11a.jpg"
}
```

{% endcode %}
{% endtab %}
{% endtabs %}

### Inpaint with ControlNet

<mark style="color:green;">`action`</mark> `img2img`

**Input Parameters**

<table><thead><tr><th>Name</th><th>Type</th><th>Description</th><th>Default</th><th data-type="checkbox">Required</th></tr></thead><tbody><tr><td>action</td><td><code>string</code></td><td>The action of ControlNet, txt2img or img2img</td><td>txt2img</td><td>true</td></tr><tr><td>model</td><td><code>string</code></td><td>The model id from civitai (SD1.5, SDXL 1.0, PlaygroundV2). How to get it? Click on a model page on civitai, and copy the series number within the download link.</td><td>64094</td><td>true</td></tr><tr><td>controlnet_model</td><td><code>string</code></td><td>The ControlNet model id from civitai. How to get it? Click on a model page on civitai, and copy the series number within the download link.</td><td>10971</td><td>true</td></tr><tr><td>image</td><td><code>string</code></td><td>The input image, can be a url or base64 sting</td><td></td><td>true</td></tr><tr><td>no_mask</td><td><code>boolean</code></td><td>Whether to use mask</td><td>False</td><td>true</td></tr><tr><td>mask</td><td><code>string</code></td><td>The correspond mask, can be a url or base64 sting. 1 for mask region</td><td></td><td>false</td></tr><tr><td>prompt</td><td><code>string</code></td><td>The text prompt for ControlNet. Add lora? add `` to your prompt. `$id` is the series number and `$weight` is the lora weight you want (always set to 1.0). You can use multiple loras.</td><td></td><td>true</td></tr><tr><td>negative_prompt</td><td><code>string</code></td><td>The negative prompt for ControlNet.</td><td>(worst quality, low quality:1.4),(malformed hands:1.4),(poorly drawn hands:1.4),(mutated fingers:1.4),(extra limbs:1.35),(poorly drawn face:1.4),bad leg,strange leg, poor eyes, full screen of face</td><td>true</td></tr><tr><td>controlnet_module</td><td><code>string</code></td><td>The ControNet module</td><td>none</td><td>true</td></tr><tr><td>guidance_start</td><td><code>number</code></td><td>ControlNet guidance start</td><td>0</td><td>true</td></tr><tr><td>guidance_end</td><td><code>number</code></td><td>ControlNet guidance end</td><td>1</td><td>true</td></tr><tr><td>control_mode</td><td><code>string</code></td><td>The improved guess mode</td><td>Balanced</td><td>true</td></tr><tr><td>weight</td><td><code>number</code></td><td>The weight of the controlnet model</td><td>1</td><td>true</td></tr><tr><td>resize_mode</td><td><code>string</code></td><td>Four modes for output shape calculation: (1) Keep: keep original shape, (2) Certain: based on input width/length (divisible by 32), (3,4) min/max ratio: keep aspect ratio, the resize factor is the min/max of (h/H, w/W).</td><td>certain</td><td>true</td></tr><tr><td>threshold_a</td><td><code>integer</code></td><td>The threshold A for controlnet model</td><td>64</td><td>true</td></tr><tr><td>threshold_b</td><td><code>integer</code></td><td>The threshold B for controlnet model</td><td>64</td><td>true</td></tr><tr><td>steps</td><td><code>integer</code></td><td>Steps for sampler to step whle sampling</td><td>25</td><td>true</td></tr><tr><td>cfg_scale</td><td><code>number</code></td><td>Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results. Default to 7.</td><td>7.0</td><td>true</td></tr><tr><td>sampler</td><td><code>string</code></td><td>Sampler for diffusion model inference</td><td>DPM++ 2M</td><td>true</td></tr><tr><td>height</td><td><code>integer</code></td><td>Height of the generated images</td><td>512</td><td>true</td></tr><tr><td>width</td><td><code>integer</code></td><td>Width of the generated images</td><td>512</td><td>true</td></tr><tr><td>seed</td><td><code>integer</code></td><td>Random seed for generation process. -1 means random seed</td><td>-1</td><td>false</td></tr><tr><td>clip_skip</td><td><code>integer</code></td><td>Early stopping parameter for CLIP model; 1 is stop at last layer as usual, 2 is stop at penultimate layer, etc.</td><td>1</td><td>true</td></tr><tr><td>mask_blur</td><td><code>integer</code></td><td>Mask blur refers to the feathering of a mask (from edges to inside the mask), adjusted between 0-64. A smaller value results in sharper edges. Default to 4</td><td>4</td><td>true</td></tr><tr><td>inpainting_fill</td><td><code>integer</code></td><td>Choose the fill content in mask: 0 - fill, 1 - original, 2 - latent noise, 3 - latent nothing</td><td>1</td><td>true</td></tr><tr><td>inpainting_mask_invert</td><td><code>integer</code></td><td>0 - Inpaint masked region, 1 - Inpaint not masked region</td><td>0</td><td>true</td></tr><tr><td>denoising_strength</td><td><code>number</code></td><td>Strength of image transfomation during inpainting precess. High means more influence during transformation</td><td>0.7</td><td>true</td></tr></tbody></table>

**Output Parameters**

| Name | Type     | Description                                                                                                  | File Type |
| ---- | -------- | ------------------------------------------------------------------------------------------------------------ | --------- |
| url  | `string` | The url of generated image, stored in the cloud. Only temporarily effective, will be cleared in a few hours. | `image`   |

**Output Example**

{% tabs %}
{% tab title="success" %}
{% code fullWidth="false" %}

```json
{
  "url": "https://image.myshell.ai/image/chat/embed_obj/7758545/202405221652/f9550811186e4152952020ea097375d6.jpg"
}
```

{% endcode %}
{% endtab %}
{% endtabs %}