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+ images/bird.png filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -1,3 +1,376 @@
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  ---
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  license: openrail
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
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  license: openrail
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  ---
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+
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+ # Controlnet
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+
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+ Controlnet is an auxiliary model which augments pre-trained diffusion models with an additional conditioning.
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+
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+ Controlnet comes with multiple auxiliary models, each which allows a different type of conditioning
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+
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+ Controlnet's auxiliary models are trained with stable diffusion 1.5. Experimentally, the auxiliary models can be used with other diffusion models such as dreamboothed stable diffusion.
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+
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+ The auxiliary conditioning is passed directly to the diffusers pipeline. If you want to process an image to create the auxiliary conditioning, external dependencies are required.
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+
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+ Some of the additional conditionings can be extracted from images via additional models. We extracted these
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+ additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/human_pose.git).
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+
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+ ## Canny edge detection
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+
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+ Install opencv
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+
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+ ```sh
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+ $ pip install opencv-contrib-python
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+ ```
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+
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+ ```python
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+ import cv2
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+ from PIL import Image
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+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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+ import torch
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+ import numpy as np
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+
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+ image = Image.open('images/bird.png')
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+ image = np.array(image)
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+
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+ low_threshold = 100
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+ high_threshold = 200
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+
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+ image = cv2.Canny(image, low_threshold, high_threshold)
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+ image = image[:, :, None]
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+ image = np.concatenate([image, image, image], axis=2)
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+ image = Image.fromarray(image)
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+
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+ controlnet = ControlNetModel.from_pretrained(
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+ "fusing/stable-diffusion-v1-5-controlnet-canny",
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+ )
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+
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+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
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+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
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+ )
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+ pipe.to('cuda')
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+
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+ image = pipe("bird", image).images[0]
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+
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+ image.save('images/bird_canny_out.png')
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+ ```
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+
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+ ![bird](./images/bird.png)
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+
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+ ![bird_canny](./images/bird_canny.png)
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+
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+ ![bird_canny_out](./images/bird_canny_out.png)
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+
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+ ## M-LSD Straight line detection
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+
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+ Install the additional controlnet models package.
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+
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+ ```sh
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+ $ pip install git+https://github.com/patrickvonplaten/human_pose.git
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+ ```
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+
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+ ```py
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+ from PIL import Image
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+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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+ import torch
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+ from human_pose import MLSDdetector
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+
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+ mlsd = MLSDdetector.from_pretrained('lllyasviel/ControlNet')
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+
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+ image = Image.open('images/room.png')
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+
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+ image = mlsd(image)
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+
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+ controlnet = ControlNetModel.from_pretrained(
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+ "fusing/stable-diffusion-v1-5-controlnet-mlsd",
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+ )
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+
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+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
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+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
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+ )
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+ pipe.to('cuda')
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+
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+ image = pipe("room", image).images[0]
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+
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+ image.save('images/room_mlsd_out.png')
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+ ```
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+
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+ ![room](./images/room.png)
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+
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+ ![room_mlsd](./images/room_mlsd.png)
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+
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+ ![room_mlsd_out](./images/room_mlsd_out.png)
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+
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+ ## Pose estimation
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+
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+ Install the additional controlnet models package.
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+
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+ ```sh
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+ $ pip install git+https://github.com/patrickvonplaten/human_pose.git
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+ ```
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+
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+ ```py
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+ from PIL import Image
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+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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+ import torch
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+ from human_pose import OpenposeDetector
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+
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+ openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
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+
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+ image = Image.open('images/pose.png')
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+
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+ image = openpose(image)
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+
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+ controlnet = ControlNetModel.from_pretrained(
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+ "fusing/stable-diffusion-v1-5-controlnet-openpose",
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+ )
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+
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+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
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+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
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+ )
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+ pipe.to('cuda')
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+
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+ image = pipe("chef in the kitchen", image).images[0]
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+
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+ image.save('images/chef_pose_out.png')
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+ ```
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+
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+ ![pose](./images/pose.png)
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+
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+ ![openpose](./images/openpose.png)
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+
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+ ![chef_pose_out](./images/chef_pose_out.png)
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+
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+ ## Semantic Segmentation
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+
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+ Semantic segmentation relies on transformers. Transformers is a
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+ dependency of diffusers for running controlnet, so you should
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+ have it installed already.
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+
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+ ```py
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+ from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
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+ from PIL import Image
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+ import numpy as np
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+ from controlnet_utils import ade_palette
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+ import torch
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+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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+
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+ image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
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+ image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
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+
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+ image = Image.open("./images/house.png").convert('RGB')
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+
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+ pixel_values = image_processor(image, return_tensors="pt").pixel_values
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+
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+ with torch.no_grad():
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+ outputs = image_segmentor(pixel_values)
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+
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+ seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
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+
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+ color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
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+
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+ palette = np.array(ade_palette())
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+
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+ for label, color in enumerate(palette):
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+ color_seg[seg == label, :] = color
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+
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+ color_seg = color_seg.astype(np.uint8)
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+
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+ image = Image.fromarray(color_seg)
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+
181
+ controlnet = ControlNetModel.from_pretrained(
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+ "fusing/stable-diffusion-v1-5-controlnet-seg",
183
+ )
184
+
185
+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
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+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
187
+ )
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+ pipe.to('cuda')
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+
190
+ image = pipe("house", image).images[0]
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+
192
+ image.save('./images/house_seg_out.png')
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+ ```
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+
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+ ![house](images/house.png)
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+
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+ ![house_seg](images/house_seg.png)
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+
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+ ![house_seg_out](images/house_seg_out.png)
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+
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+ ## Depth control
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+
203
+ Depth control relies on transformers. Transformers is a dependency of diffusers for running controlnet, so
204
+ you should have it installed already.
205
+
206
+ ```py
207
+ from transformers import pipeline
208
+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
209
+ from PIL import Image
210
+ import numpy as np
211
+
212
+ depth_estimator = pipeline('depth-estimation')
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+
214
+ image = Image.open('./images/stormtrooper.png')
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+ image = depth_estimator(image)['depth']
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+ image = np.array(image)
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+ image = image[:, :, None]
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+ image = np.concatenate([image, image, image], axis=2)
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+ image = Image.fromarray(image)
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+
221
+ controlnet = ControlNetModel.from_pretrained(
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+ "fusing/stable-diffusion-v1-5-controlnet-depth",
223
+ )
224
+
225
+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
226
+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
227
+ )
228
+ pipe.to('cuda')
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+
230
+ image = pipe("Stormtrooper's lecture", image).images[0]
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+
232
+ image.save('./images/stormtrooper_depth_out.png')
233
+ ```
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+
235
+ ![stormtrooper](./images/stormtrooper.png)
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+
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+ ![stormtrooler_depth](./images/stormtrooper_depth.png)
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+
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+ ![stormtrooler_depth_out](./images/stormtrooper_depth_out.png)
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+
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+
242
+ ## Normal map
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+
244
+ ```py
245
+ from PIL import Image
246
+ from transformers import pipeline
247
+ import numpy as np
248
+ import cv2
249
+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
250
+
251
+ image = Image.open("images/toy.png").convert("RGB")
252
+
253
+ depth_estimator = pipeline("depth-estimation", model ="Intel/dpt-hybrid-midas" )
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+
255
+ image = depth_estimator(image)['predicted_depth'][0]
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+
257
+ image = image.numpy()
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+
259
+ image_depth = image.copy()
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+ image_depth -= np.min(image_depth)
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+ image_depth /= np.max(image_depth)
262
+
263
+ bg_threhold = 0.4
264
+
265
+ x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
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+ x[image_depth < bg_threhold] = 0
267
+
268
+ y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)
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+ y[image_depth < bg_threhold] = 0
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+
271
+ z = np.ones_like(x) * np.pi * 2.0
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+
273
+ image = np.stack([x, y, z], axis=2)
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+ image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5
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+ image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
276
+ image = Image.fromarray(image)
277
+
278
+ controlnet = ControlNetModel.from_pretrained(
279
+ "fusing/stable-diffusion-v1-5-controlnet-normal",
280
+ )
281
+
282
+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
283
+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
284
+ )
285
+ pipe.to('cuda')
286
+
287
+ image = pipe("cute toy", image).images[0]
288
+
289
+ image.save('images/toy_normal_out.png')
290
+ ```
291
+
292
+ ![toy](./images/toy.png)
293
+
294
+ ![toy_normal](./images/toy_normal.png)
295
+
296
+ ![toy_normal_out](./images/toy_normal_out.png)
297
+
298
+ ## Scribble
299
+
300
+ Install the additional controlnet models package.
301
+
302
+ ```sh
303
+ $ pip install git+https://github.com/patrickvonplaten/human_pose.git
304
+ ```
305
+
306
+ ```py
307
+ from PIL import Image
308
+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
309
+ import torch
310
+ from human_pose import HEDdetector
311
+
312
+ hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
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+
314
+ image = Image.open('images/bag.png')
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+
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+ image = hed(image, scribble=True)
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+
318
+ controlnet = ControlNetModel.from_pretrained(
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+ "fusing/stable-diffusion-v1-5-controlnet-scribble",
320
+ )
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+
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+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
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+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
324
+ )
325
+ pipe.to('cuda')
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+
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+ image = pipe("bag", image).images[0]
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+
329
+ image.save('images/bag_scribble_out.png')
330
+ ```
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+
332
+ ![bag](./images/bag.png)
333
+
334
+ ![bag_scribble](./images/bag_scribble.png)
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+
336
+ ![bag_scribble_out](./images/bag_scribble_out.png)
337
+
338
+ ## HED Boundary
339
+
340
+ Install the additional controlnet models package.
341
+
342
+ ```sh
343
+ $ pip install git+https://github.com/patrickvonplaten/human_pose.git
344
+ ```
345
+
346
+ ```py
347
+ from PIL import Image
348
+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
349
+ import torch
350
+ from human_pose import HEDdetector
351
+
352
+ hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
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+
354
+ image = Image.open('images/man.png')
355
+
356
+ image = hed(image)
357
+
358
+ controlnet = ControlNetModel.from_pretrained(
359
+ "fusing/stable-diffusion-v1-5-controlnet-hed",
360
+ )
361
+
362
+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
363
+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
364
+ )
365
+ pipe.to('cuda')
366
+
367
+ image = pipe("oil painting of handsome old man, masterpiece", image).images[0]
368
+
369
+ image.save('images/man_hed_out.png')
370
+ ```
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+
372
+ ![man](./images/man.png)
373
+
374
+ ![man_hed](./images/man_hed.png)
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+
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+ ![man_hed_out](./images/man_hed_out.png)
controlnet_utils.py ADDED
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+ def ade_palette():
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+ """ADE20K palette that maps each class to RGB values."""
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+ return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
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+ [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
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+ [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
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+ [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
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+ [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
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+ [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
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+ [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
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+ [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
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+ [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
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+ [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
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+ [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
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+ [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
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+ [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
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+ [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
17
+ [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
18
+ [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
19
+ [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
20
+ [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
21
+ [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
22
+ [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
23
+ [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
24
+ [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
25
+ [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
26
+ [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
27
+ [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
28
+ [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
29
+ [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
30
+ [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
31
+ [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
32
+ [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
33
+ [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
34
+ [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
35
+ [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
36
+ [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
37
+ [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
38
+ [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
39
+ [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
40
+ [102, 255, 0], [92, 0, 255]]
images/bag.png ADDED
images/bag_scribble.png ADDED
images/bag_scribble_out.png ADDED
images/bird.png ADDED

Git LFS Details

  • SHA256: cad49fc7d3071b2bcd078bc8dde365f8fa62eaa6d43705fd50c212794a3aac35
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  • Size of remote file: 1.07 MB
images/bird_canny.png ADDED
images/bird_canny_out.png ADDED
images/chef_pose_out.png ADDED
images/house.png ADDED
images/house_seg.png ADDED
images/house_seg_out.png ADDED
images/man.png ADDED
images/man_hed.png ADDED
images/man_hed_out.png ADDED
images/openpose.png ADDED
images/pose.png ADDED
images/room.png ADDED
images/room_mlsd.png ADDED
images/room_mlsd_out.png ADDED
images/stormtrooper.png ADDED
images/stormtrooper_depth.png ADDED
images/stormtrooper_depth_out.png ADDED
images/toy.png ADDED
images/toy_normal.png ADDED
images/toy_normal_out.png ADDED