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Browse files- .gitattributes +1 -0
- README.md +373 -0
- controlnet_utils.py +40 -0
- images/bag.png +0 -0
- images/bag_scribble.png +0 -0
- images/bag_scribble_out.png +0 -0
- images/bird.png +3 -0
- 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.png +0 -0
- images/stormtrooper_depth.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
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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images/bird.png filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: openrail
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---
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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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Controlnet is an auxiliary model which augments pre-trained diffusion models with an additional conditioning.
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Controlnet comes with multiple auxiliary models, each which allows a different type of conditioning
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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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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/human_pose.git).
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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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+
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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.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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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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)
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pipe.to('cuda')
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image = pipe("Stormtrooper's lecture", image).images[0]
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image.save('./images/stormtrooper_depth_out.png')
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```
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![stormtrooper](./images/stormtrooper.png)
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![stormtrooler_depth](./images/stormtrooper_depth.png)
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![stormtrooler_depth_out](./images/stormtrooper_depth_out.png)
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|
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## Normal map
|
243 |
+
|
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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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+
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depth_estimator = pipeline("depth-estimation", model ="Intel/dpt-hybrid-midas" )
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+
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image = depth_estimator(image)['predicted_depth'][0]
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+
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image = image.numpy()
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+
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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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+
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bg_threhold = 0.4
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+
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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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+
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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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275 |
+
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')
|
313 |
+
|
314 |
+
image = Image.open('images/bag.png')
|
315 |
+
|
316 |
+
image = hed(image, scribble=True)
|
317 |
+
|
318 |
+
controlnet = ControlNetModel.from_pretrained(
|
319 |
+
"fusing/stable-diffusion-v1-5-controlnet-scribble",
|
320 |
+
)
|
321 |
+
|
322 |
+
pipe = StableDiffusionControlNetPipeline.from_pretrained(
|
323 |
+
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
|
324 |
+
)
|
325 |
+
pipe.to('cuda')
|
326 |
+
|
327 |
+
image = pipe("bag", image).images[0]
|
328 |
+
|
329 |
+
image.save('images/bag_scribble_out.png')
|
330 |
+
```
|
331 |
+
|
332 |
+
![bag](./images/bag.png)
|
333 |
+
|
334 |
+
![bag_scribble](./images/bag_scribble.png)
|
335 |
+
|
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')
|
353 |
+
|
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 |
+
```
|
371 |
+
|
372 |
+
![man](./images/man.png)
|
373 |
+
|
374 |
+
![man_hed](./images/man_hed.png)
|
375 |
+
|
376 |
+
![man_hed_out](./images/man_hed_out.png)
|
controlnet_utils.py
ADDED
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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],
|
5 |
+
[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],
|
7 |
+
[143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
|
8 |
+
[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],
|
10 |
+
[255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
|
11 |
+
[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],
|
13 |
+
[255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
|
14 |
+
[6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
|
15 |
+
[140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
|
16 |
+
[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
|
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