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---
license: apache-2.0
tags:
- openpose
- controlnet
- diffusers
- controlnet-openpose-sdxl-1.0
- text_to_image
---
# ***State of the art ControlNet-openpose-sdxl-1.0 model, not limited to anime, just for show***
### controlnet-openpose-sdxl-1.0
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** xinsir
- **Model type:** ControlNet_SDXL
- **License:** apache-2.0
- **Finetuned from model [optional]:** stabilityai/stable-diffusion-xl-base-1.0
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Paper [optional]:** https://arxiv.org/abs/2302.05543
-
### Examples
## 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. |