The ControlNet-openpose-sdxl-1.0 model is a state-of-the-art development in the field of AI, specifically focused on text-to-image generation. This model was developed by xinsir and licensed under the Apache 2.0. The results it produces, particularly for Midjourney and anime styles, are visually striking. In this guide, we’ll delve into how to get started with the model, enhancing your experience and results.

A Glimpse of the Model’s Capabilities

Before jumping into the technicalities, take a moment to appreciate the kinds of results this model can produce:

Understanding the Model

At its core, ControlNet-openpose-sdxl-1.0 applies advanced techniques to generate images based on specific text prompts. By using open pose detection, it effectively interprets body positions and movements, granting it the ability to create human-like figures in various artistic styles.

Getting Started: Installation and Setup

To utilize this model, you need to run a specific piece of code. It initializes the environment, loads the necessary components, and handles the image generation process efficiently. Here’s a breakdown of the base code you’ll need to get started:

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)
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')

# Resize image to 1024 x 1024
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("your image save path, png format is usually better than jpg or webp in terms of image quality but got much bigger")

The above code is like preparing a gourmet recipe. Each ingredient (or library) plays a crucial role in achieving the delicious outcome (or generated image). If you mix them properly and follow the steps correctly, you’ll bring a masterpiece to life!

How to Replace the Default Draw Pose Function

To enhance the performance of the pose detection in your images, it is recommended to replace the default draw pose function. This can significantly improve the visual stability of the pose in your outputs. Follow these steps:

• Locate the util.py file in the controlnet_aux package, usually found at the path: /your anaconda3 path/envs/your env name/lib/python3.8/site-packages/controlnet_aux/open_pose/util.py
• Overwrite the existing draw_bodypose function with the provided implementation in the README.

Sample Implementation of draw_bodypose

def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint]) -> np.ndarray:
    # Function implementation...
    return canvas

Troubleshooting Tips

If you encounter issues with unstable performance while using the model, ensure you’ve replaced the default pose drawing function as described. Additionally, check if your image resolutions match the standards outlined in the code. Inconsistent dimensions can often lead to unexpected results.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

This state-of-the-art model opens exciting doors for creators looking to generate captivating images with precision in body poses. Remember to adjust the draw pose function to enhance your results. Happy coding!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.