| from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL |
| from diffusers.utils import load_image |
| from PIL import Image |
| import torch |
| import numpy as np |
| import cv2 |
|
|
| prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting" |
| negative_prompt = 'low quality, bad quality, sketches' |
|
|
| image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png") |
|
|
| controlnet_conditioning_scale = 0.5 |
|
|
| controlnet = ControlNetModel.from_pretrained( |
| "diffusers/controlnet-canny-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, |
| torch_dtype=torch.float16, |
| ) |
| pipe.enable_model_cpu_offload() |
|
|
| image = np.array(image) |
| image = cv2.Canny(image, 100, 200) |
| image = image[:, :, None] |
| image = np.concatenate([image, image, image], axis=2) |
| image = Image.fromarray(image) |
|
|
|
|
|
|
