| 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") | |