controlnet-depth-sdxl-1.0

from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers import DDIMScheduler, EulerAncestralDiscreteScheduler
from PIL import Image
import torch
import random
import numpy as np
import cv2

from controlnet_aux import MidasDetector, ZoeDetector


processor_zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
processor_midas = MidasDetector.from_pretrained("lllyasviel/Annotators")


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-depth-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,
)

# need to resize the image resolution to 1024 * 1024 or same bucket resolution to get the best performance

img = cv2.imread("your original image path")

if random.random() > 0.5:
controlnet_img = processor_zoe(img, output_type='cv2')
else:
controlnet_img = processor_midas(img, output_type='cv2')


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")