Create README.md

README.md

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+---
+language:
+- en
+---
+```python
+import paddle
+from ppdiffusers import DiffusionPipeline, ControlNetModel
+from ppdiffusers.utils import load_image, image_grid
+import numpy as np
+from PIL import Image
+import cv2
+
+class CannyDetector:
+    def __call__(self, img, low_threshold, high_threshold):
+        return cv2.Canny(img, low_threshold, high_threshold)
+apply_canny = CannyDetector()
+
+# 加载模型
+controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", paddle_dtype=paddle.float16)
+pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", 
+                                         controlnet=controlnet,
+                                         safety_checker=None,
+                                         feature_extractor=None,
+                                         requires_safety_checker=False,
+                                         paddle_dtype=paddle.float16, 
+                                         custom_pipeline="webui_stable_diffusion_controlnet", 
+                                         custom_revision="9aa0fcae034d99a796c3077ec6fea84808fc5875")
+
+# 或者 # custom_pipeline="junnyu/webui_controlnet_ppdiffusers")
+
+# 加载图片
+raw_image = load_image("https://paddlenlp.bj.bcebos.com/models/community/junnyu/develop/control_bird_canny_demo.png")
+canny_image = Image.fromarray(apply_canny(np.array(raw_image), low_threshold=100, high_threshold=200))
+
+# 选择sampler
+# Please choose in ['pndm', 'lms', 'euler', 'euler-ancestral', 'dpm-multi', 'dpm-single', 'unipc-multi', 'ddim', 'ddpm', 'deis-multi', 'heun', 'kdpm2-ancestral', 'kdpm2']!
+pipe.switch_scheduler('euler-ancestral')
+
+# propmpt 和 negative_prompt
+prompt = "a (blue:1.5) bird"
+negative_prompt = ""
+# 想要返回多少张图片
+num = 4
+clip_skip = 2
+controlnet_conditioning_scale = 1.
+num_inference_steps = 50
+
+all_images = []
+print("raw_image vs canny_image")
+display(image_grid([raw_image, canny_image], 1, 2))
+for i in range(num):
+    img = pipe(
+        prompt=prompt,
+        negative_prompt = negative_prompt,
+        image=canny_image,
+        num_inference_steps=num_inference_steps,
+        controlnet_conditioning_scale=controlnet_conditioning_scale,
+        clip_skip= clip_skip,
+    ).images[0]
+    all_images.append(img)
+display(image_grid(all_images, 1, num))
+```
+
+![image](https://user-images.githubusercontent.com/50394665/231669030-ae9302f0-3110-4021-83a9-66a7218dfcc9.png)