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from diffusers.utils import load_image
import cv2
from PIL import Image
import numpy as np
from diffusers import UniPCMultistepScheduler
from pipeline_onnx_stable_diffusion_controlnet import OnnxStableDiffusionControlNetPipeline
import onnxruntime as ort
image = load_image(
"input_image_vermeer.png"
)
image = np.array(image)
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
opts = ort.SessionOptions()
opts.enable_cpu_mem_arena = False
opts.enable_mem_pattern = False
pipe = OnnxStableDiffusionControlNetPipeline.from_pretrained(
"model/sd1_5-fp16-vae_ft_mse-autoslicing-cn_canny",
sess_options=opts,
provider="DmlExecutionProvider",
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
prompt = "jpop singer on stage, best quality, extremely detailed"
seed=42
generator = np.random.RandomState(seed)
images = pipe(
prompt,
canny_image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=20,
generator=generator,
).images[0]
images.save("controlnet-canny-test.png")
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