Commit
•
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Parent(s):
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README update
Browse files- README.md +19 -324
- controlnet_utils.py +0 -40
- images/bag.png +0 -0
- images/bag_scribble.png +0 -0
- images/bag_scribble_out.png +0 -0
- images/bird.png +0 -3
- images/bird_canny.png +0 -0
- images/bird_canny_out.png +0 -0
- images/chef_pose_out.png +0 -0
- images/house.png +0 -0
- images/house_seg.png +0 -0
- images/house_seg_out.png +0 -0
- images/man.png +0 -0
- images/man_hed.png +0 -0
- images/man_hed_out.png +0 -0
- images/openpose.png +0 -0
- images/pose.png +0 -0
- images/room.png +0 -0
- images/room_mlsd.png +0 -0
- images/room_mlsd_out.png +0 -0
- images/stormtrooper_depth_out.png +0 -0
- images/toy.png +0 -0
- images/toy_normal.png +0 -0
- images/toy_normal_out.png +0 -0
README.md
CHANGED
@@ -18,201 +18,21 @@ Controlnet's auxiliary models are trained with stable diffusion 1.5. Experimenta
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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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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/
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## Canny edge detection
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Install opencv
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```sh
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$ pip install opencv-contrib-python
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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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image = Image.open('images/bird.png')
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image = np.array(image)
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low_threshold = 100
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high_threshold = 200
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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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controlnet = ControlNetModel.from_pretrained(
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"fusing/stable-diffusion-v1-5-controlnet-canny",
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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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image = pipe("bird", image).images[0]
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image.save('images/bird_canny_out.png')
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```
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![bird](./images/bird.png)
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![bird_canny](./images/bird_canny.png)
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![bird_canny_out](./images/bird_canny_out.png)
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## M-LSD Straight line detection
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Install the additional controlnet models package.
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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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```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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mlsd = MLSDdetector.from_pretrained('lllyasviel/ControlNet')
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image = Image.open('images/room.png')
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image = mlsd(image)
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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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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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image = pipe("room", image).images[0]
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image.save('images/room_mlsd_out.png')
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```
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![room](./images/room.png)
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![room_mlsd](./images/room_mlsd.png)
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![room_mlsd_out](./images/room_mlsd_out.png)
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## Pose estimation
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Install the additional controlnet models package.
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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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```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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openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
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image = Image.open('images/pose.png')
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image = openpose(image)
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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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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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image = pipe("chef in the kitchen", image).images[0]
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image.save('images/chef_pose_out.png')
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```
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![pose](./images/pose.png)
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![openpose](./images/openpose.png)
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![chef_pose_out](./images/chef_pose_out.png)
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## Semantic Segmentation
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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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```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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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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image = Image.open("./images/house.png").convert('RGB')
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pixel_values = image_processor(image, return_tensors="pt").pixel_values
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with torch.no_grad():
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outputs = image_segmentor(pixel_values)
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seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
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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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palette = np.array(ade_palette())
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for label, color in enumerate(palette):
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color_seg[seg == label, :] = color
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color_seg = color_seg.astype(np.uint8)
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image = Image.fromarray(color_seg)
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controlnet = ControlNetModel.from_pretrained(
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"fusing/stable-diffusion-v1-5-controlnet-seg",
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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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image = pipe("house", image).images[0]
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image.save('./images/house_seg_out.png')
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```
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![house](images/house.png)
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![house_seg](images/house_seg.png)
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![house_seg_out](images/house_seg_out.png)
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## Depth control
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Depth control relies on transformers. Transformers is a dependency of diffusers for running controlnet, so
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you should have it installed already.
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```py
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from transformers import pipeline
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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from PIL import Image
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import numpy as np
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depth_estimator = pipeline('depth-estimation')
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image = Image.fromarray(image)
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controlnet = ControlNetModel.from_pretrained(
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"fusing/stable-diffusion-v1-5-controlnet-depth",
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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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pipe.to('cuda')
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image.save('./images/stormtrooper_depth_out.png')
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```
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![stormtrooler_depth_out](./images/stormtrooper_depth_out.png)
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```py
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from PIL import Image
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from transformers import pipeline
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import numpy as np
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import cv2
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
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image = Image.open("images/toy.png").convert("RGB")
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depth_estimator = pipeline("depth-estimation", model ="Intel/dpt-hybrid-midas" )
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image = depth_estimator(image)['predicted_depth'][0]
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image = image.numpy()
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image_depth = image.copy()
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image_depth -= np.min(image_depth)
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image_depth /= np.max(image_depth)
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bg_threhold = 0.4
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x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
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x[image_depth < bg_threhold] = 0
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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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z = np.ones_like(x) * np.pi * 2.0
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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)
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image = Image.fromarray(image)
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controlnet = ControlNetModel.from_pretrained(
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"fusing/stable-diffusion-v1-5-controlnet-normal",
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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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image = pipe("cute toy", image).images[0]
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image.save('images/toy_normal_out.png')
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```
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![toy](./images/toy.png)
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![toy_normal](./images/toy_normal.png)
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![toy_normal_out](./images/toy_normal_out.png)
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## Scribble
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Install the additional controlnet models package.
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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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```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 HEDdetector
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hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
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image = Image.open('images/bag.png')
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image = hed(image, scribble=True)
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controlnet = ControlNetModel.from_pretrained(
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"fusing/stable-diffusion-v1-5-controlnet-scribble",
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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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image = pipe("bag", image).images[0]
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image.save('images/bag_scribble_out.png')
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```
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![bag](./images/bag.png)
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![bag_scribble](./images/bag_scribble.png)
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![bag_scribble_out](./images/bag_scribble_out.png)
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## HED Boundary
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Install the additional controlnet models package.
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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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```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 HEDdetector
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hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
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image = Image.open('images/man.png')
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image = hed(image)
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controlnet = ControlNetModel.from_pretrained(
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"fusing/stable-diffusion-v1-5-controlnet-hed",
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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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image = pipe("oil painting of handsome old man, masterpiece", image).images[0]
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image.save('images/man_hed_out.png')
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```
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![man](./images/man.png)
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![man_hed](./images/man_hed.png)
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![man_hed_out](./images/man_hed_out.png)
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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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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/controlnet_aux.git).
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## Depth control
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+
### Diffusers
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+
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Depth control relies on transformers. Transformers is a dependency of diffusers for running controlnet, so
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you should have it installed already.
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```py
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from transformers import pipeline
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+
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
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from PIL import Image
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import numpy as np
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+
import torch
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depth_estimator = pipeline('depth-estimation')
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image = Image.fromarray(image)
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controlnet = ControlNetModel.from_pretrained(
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+
"fusing/stable-diffusion-v1-5-controlnet-depth", torch_dtype=torch.float16
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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, torch_dtype=torch.float16
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)
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+
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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+
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+
# Remove if you do not have xformers installed
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+
# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
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+
# for installation instructions
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+
pipe.enable_xformers_memory_efficient_attention()
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+
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+
pipe.enable_model_cpu_offload()
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+
|
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+
image = pipe("Stormtrooper's lecture", image, num_inference_steps=20).images[0]
|
64 |
|
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image.save('./images/stormtrooper_depth_out.png')
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```
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71 |
|
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![stormtrooler_depth_out](./images/stormtrooper_depth_out.png)
|
73 |
|
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+
### Training
|
75 |
|
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+
The depth model was trained on 3M depth-image, caption pairs. The depth images were generated with Midas. The model was trained for 500 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model.
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controlnet_utils.py
DELETED
@@ -1,40 +0,0 @@
|
|
1 |
-
def ade_palette():
|
2 |
-
"""ADE20K palette that maps each class to RGB values."""
|
3 |
-
return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
|
4 |
-
[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],
|
6 |
-
[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],
|
9 |
-
[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],
|
12 |
-
[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],
|
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-
[255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
|
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-
[11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
|
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-
[0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
|
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-
[255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
|
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-
[0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
|
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-
[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]]
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images/bag.png
DELETED
Binary file (462 kB)
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images/bag_scribble.png
DELETED
Binary file (11 kB)
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images/bag_scribble_out.png
DELETED
Binary file (556 kB)
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images/bird.png
DELETED
Git LFS Details
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images/bird_canny.png
DELETED
Binary file (29.1 kB)
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images/bird_canny_out.png
DELETED
Binary file (845 kB)
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images/chef_pose_out.png
DELETED
Binary file (570 kB)
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images/house.png
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images/house_seg.png
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Binary file (3.68 kB)
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images/house_seg_out.png
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Binary file (472 kB)
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images/man.png
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images/man_hed.png
DELETED
Binary file (118 kB)
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images/man_hed_out.png
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images/openpose.png
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images/pose.png
DELETED
Binary file (592 kB)
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images/room.png
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images/room_mlsd.png
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Binary file (9.06 kB)
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images/room_mlsd_out.png
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Binary file (575 kB)
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images/stormtrooper_depth_out.png
CHANGED
![]() |
![]() |
images/toy.png
DELETED
Binary file (312 kB)
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images/toy_normal.png
DELETED
Binary file (90.1 kB)
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images/toy_normal_out.png
DELETED
Binary file (231 kB)
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