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--- |
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language: |
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- en |
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--- |
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```python |
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import paddle |
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from ppdiffusers import DiffusionPipeline, ControlNetModel |
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from ppdiffusers.utils import load_image, image_grid |
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import numpy as np |
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from PIL import Image |
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import cv2 |
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class CannyDetector: |
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def __call__(self, img, low_threshold, high_threshold): |
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return cv2.Canny(img, low_threshold, high_threshold) |
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apply_canny = CannyDetector() |
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# 加载模型 |
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controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", paddle_dtype=paddle.float16) |
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", |
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controlnet=controlnet, |
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safety_checker=None, |
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feature_extractor=None, |
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requires_safety_checker=False, |
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paddle_dtype=paddle.float16, |
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custom_pipeline="webui_stable_diffusion_controlnet", |
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custom_revision="9aa0fcae034d99a796c3077ec6fea84808fc5875") |
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# 或者 # custom_pipeline="junnyu/webui_controlnet_ppdiffusers") |
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# 加载图片 |
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raw_image = load_image("https://paddlenlp.bj.bcebos.com/models/community/junnyu/develop/control_bird_canny_demo.png") |
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canny_image = Image.fromarray(apply_canny(np.array(raw_image), low_threshold=100, high_threshold=200)) |
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# 选择sampler |
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# Please choose in ['pndm', 'lms', 'euler', 'euler-ancestral', 'dpm-multi', 'dpm-single', 'unipc-multi', 'ddim', 'ddpm', 'deis-multi', 'heun', 'kdpm2-ancestral', 'kdpm2']! |
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pipe.switch_scheduler('euler-ancestral') |
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# propmpt 和 negative_prompt |
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prompt = "a (blue:1.5) bird" |
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negative_prompt = "" |
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# 想要返回多少张图片 |
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num = 4 |
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clip_skip = 2 |
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controlnet_conditioning_scale = 1. |
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num_inference_steps = 50 |
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all_images = [] |
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print("raw_image vs canny_image") |
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display(image_grid([raw_image, canny_image], 1, 2)) |
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for i in range(num): |
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img = pipe( |
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prompt=prompt, |
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negative_prompt = negative_prompt, |
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image=canny_image, |
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num_inference_steps=num_inference_steps, |
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controlnet_conditioning_scale=controlnet_conditioning_scale, |
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clip_skip= clip_skip, |
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).images[0] |
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all_images.append(img) |
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display(image_grid(all_images, 1, num)) |
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``` |
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