---
license: other
license_name: stabilityai-ai-community
license_link: LICENSE.md
language:
- en
base_model:
- stabilityai/stable-diffusion-3.5-medium
pipeline_tag: text-to-image
---

<div align="center">

**Bokeh_Pose_Controlnet**

<img src="show.jpg"/>
</div>

## Description

- Input Image: Images processed by openpose/dwpose detection
- Output Image: Generated images with pose control

Openpose enables precise human body structure control, demonstrating better control effects and generalization compared to sd1.5 cn in our tests, and can easily adapt to lora models

## Example
| input | output | Prompt |
|:---:|:---:|:---|
| <img src="./images/001_pose.png" width="300"/> | <img src="./images/001.png" width="300"/> | 1 girl , thinking |
| <img src="./images/002_pose.png" width="300"/> | <img src="./images/002.png" width="300"/> | a man |
| <img src="./images/003_pose.png" width="300"/> | <img src="./images/003.png" width="300"/> | a young woman in room,wear a brown short,golden short hair |
| <img src="./images/004_pose.png" width="300"/> | <img src="./images/004.png" width="300"/> | a man in room,wear a brown vintage shirt,1990s |

## Use 
We recommend using ComfyUI for local inference
![input](./comfy.png)

# With Bokeh
```python
import torch
from diffusers import StableDiffusion3ControlNetPipeline
from diffusers import SD3ControlNetModel
from diffusers.utils import load_image

controlnet = SD3ControlNetModel.from_pretrained("tensorart/Bokeh_Openpose_Controlnet")
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
    "tensorart/bokeh_3.5_medium",
    controlnet=controlnet
)
pipe.to("cuda", torch.float16)

control_image = load_image("https://huggingface.co/tensorart/Bokeh_Pose_Control/resolve/main/images/001_pose.png")
prompt = "A woman thinking"
negative_prompt ="anime,render,cartoon,3d"
negative_prompt_3=""

image = pipe(
    prompt, 
    num_inference_steps=30,
    negative_prompt=negative_prompt, 
    control_image=control_image, 
    height=1728,
    width=1152,
    guidance_scale=4,
    controlnet_conditioning_scale=0.8
).images[0]
image.save('image.jpg')
```

## Contact
* Website: https://tensor.art  https://tusiart.com
* Developed by: TensorArt
* Api: https://tams.tensor.art/