---
license: apache-2.0
---
# ***State of the art ControlNet-openpose-sdxl-1.0 model, not limited to anime, just for show***
![images](./masonry0.webp)

### Examples
![images0](./000001_scribble_concat.webp)
![images1](./000003_scribble_concat.webp)
![images2](./000005_scribble_concat.webp)
![images3](./000008_scribble_concat.webpp)
![images4](./000015_scribble_concat.webp)
![images5](./000031_scribble_concat.webp)
![images6](./000042_scribble_concat.webp)
![images7](./000047_scribble_concat.webp)
![images8](./000048_scribble_concat.webp)
![images9](./000083_scribble_concat.webp)

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from controlnet_aux import OpenposeDetector
from PIL import Image
import torch
import numpy as np
import cv2



controlnet_conditioning_scale = 1.0  
prompt = "your prompt, the longer the better, you can describe it as detail as possible"
negative_prompt = 'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'



eulera_scheduler = EulerAncestralDiscreteScheduler.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", subfolder="scheduler")


controlnet = ControlNetModel.from_pretrained(
    "xinsir/controlnet-openpose-sdxl-1.0",
    torch_dtype=torch.float16
)

# when test with other base model, you need to change the vae also.
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)


pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet,
    vae=vae,
    safety_checker=None,
    torch_dtype=torch.float16,
    scheduler=eulera_scheduler,
)

processor = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')


controlnet_img = cv2.imread("your image path")
controlnet_img = processor(controlnet_img, hand_and_face=False, output_type='cv2')


# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance
height, width, _  = controlnet_img.shape
ratio = np.sqrt(1024. * 1024. / (width * height))
new_width, new_height = int(width * ratio), int(height * ratio)
controlnet_img = cv2.resize(controlnet_img, (new_width, new_height))
controlnet_img = Image.fromarray(controlnet_img)

images = pipe(
    prompt,
    negative_prompt=negative_prompt,
    image=controlnet_img,
    controlnet_conditioning_scale=controlnet_conditioning_scale,
    width=new_width,
    height=new_height,
    num_inference_steps=30,
    ).images

images[0].save(f"your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")
```


## Evaluation Data
HumanArt [https://github.com/IDEA-Research/HumanArt], select 2000 images with ground truth pose annotations to generate images and calculate mAP.



## Quantitative Result
| metric | xinsir/controlnet-openpose-sdxl-1.0 |  lllyasviel/control_v11p_sd15_openpose | thibaud/controlnet-openpose-sdxl-1.0 |
|-------|-------|-------|-------|
| mAP | **0.357** | 0.326 | 0.209 |

We are the SOTA openpose model compared with other opensource models.