pip install --upgrade ppdiffusers -f https://www.paddlepaddle.org.cn/whl/paddlenlp.html --user

git clone https://github.com/PaddlePaddle/PaddleNLPcd PaddleNLP/ppdiffuserspython setup.py install

import paddlefrom ppdiffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
paddle.seed(12321)pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)pipe.unet.load_attn_procs("Xiejiehang/CityParkLoraV1", from_hf_hub=True)image = pipe("A wetland park in the suburbs of the city, high resolution,hyper quality,full details, natural, communtiy park, outdoor, grassland", num_inference_steps=50).images[0]

import osimport cv2import randomimport paddle
from annotator.canny import CannyDetectorfrom annotator.util import HWC3, resize_imagefrom paddlenlp.trainer import set_seed as seed_everythingfrom ppdiffusers import ControlNetModel, StableDiffusionControlNetPipeline, StableDiffusionPipeline
apply_canny = CannyDetector()controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny")
pipe = StableDiffusionControlNetPipeline.from_pretrained("SG161222/Realistic_Vision_V2.0", controlnet=controlnet, from_hf_hub=True, from_diffusers=True)
def process(input_image,prompt,a_prompt,n_prompt,num_samples,image_resolution,ddim_steps,guess_mode,strength,scale,seed,eta,low_threshold,high_threshold,):
with paddle.no_grad():img = resize_image(HWC3(input_image), image_resolution)H, W, C = img.shapedetected_map = apply_canny(img, low_threshold, high_threshold)
detected_map = HWC3(detected_map)control = paddle.to_tensor(detected_map.copy(), dtype=paddle.float32) / 255.0control = control.unsqueeze(0).transpose([0, 3, 1, 2])control_scales = ([strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)) if seed == -1:seed = random.randint(0, 65535)seed_everything(seed)results = []for _ in range(num_samples):img = pipe(prompt + ", " + a_prompt,negative_prompt=n_prompt,image=control,num_inference_steps=ddim_steps,height=H,width=W,eta=eta,controlnet_conditioning_scale=control_scales,guidance_scale=scale,).images[0]results.append(img)
return [255 - detected_map] + results
inputImage = cv2.imread('test_img/village.jpg')results = process(input_image=inputImage,prompt="beautiful village,shrubs and flowers around the building,countryside,country road,blue sky,modern house,white wall,glass window, wooden roof,high resolution,hyper quality,full details",a_prompt="",n_prompt="",num_samples=1,image_resolution=512,ddim_steps=20,guess_mode=False,strength=1.0,scale=9.0,seed=123321123,eta=0.0,low_threshold=20,high_threshold=200,)
# 保存图像savePath = "./outputImg/"if not os.path.exists(savePath):os.makedirs(savePath)for i in range(1, len(results)):results[i].save(os.path.join(savePath, "{}.jpg".format(i)))

pipe.apply_lora("/home/aistudio/data/data214847/Fair-faced-concrete-V1.safetensors")
inputImage = cv2.imread('test_img/draw.jpg')results = process(input_image=inputImage,prompt="garden in residential area,large grassland,adults and children walking,people sit under umbrellas chatting,glass window,blue sky,high resolution,hyper quality,full details,modern architecture,outside,facade",a_prompt="",n_prompt="water,lake",num_samples=1,image_resolution=512,ddim_steps=20,guess_mode=False,strength=1.0,scale=9.0,seed=12332,eta=0.0,low_threshold=20,high_threshold=200,)

savePath = "./outputImg/"if not os.path.exists(savePath):os.makedirs(savePath)for i in range(1, len(results)):results[i].save(os.path.join(savePath, "{}.jpg".format(i)))