# ControlNet

With Auto1111SDK, you can use ControlNet as follows:

1. Initialize a ControlNet model first. This can be done as follows:

```python
from auto1111sdk import ControlNetModel

model = ControlNetModel(model="<THE CONTROLNET MODEL FILE NAME (WITHOUT EXTENSION)>", 
                   image="<PATH TO IMAGE>")
```

&#x20;An example of this would be:

```
model = ControlNet(model="control_v11p_sd15_openpose", 
                   image="stock_mountain.png")
```

A full list of parameters is shown below:

<pre class="language-python"><code class="lang-python">model: str # The name of the model
image: str # The path to the image
weight: float = 1.0
resize_mode: int = 1
lowvram: bool = False
processor_res: int = 64
threshold_a: int = 64
threshold_b: int = 64
<strong>guidance_start: float = 0.0
</strong>guidance_end: float = 1.0
control_mode: int = 0
pixel_perfect: bool = False
</code></pre>

2. Add the controlnet model to the StableDiffusionPipeline for text-to-image or image-to-image

```
pipe = StableDiffusionPipeline("dreamshaper.safetensors", controlnet=model)
prompt = "closeup portrait photo of black dog, 8k uhd, high quality, cinematic"
negative_prompt = "(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck"
output = pipe.generate_txt2img(num_images = 1, prompt = prompt, negative_prompt = negative_prompt, steps = 20)

output[0].save("huh.png")
```