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license: openrail

Controlnet

Controlnet is an auxiliary model which augments pre-trained diffusion models with an additional conditioning.

Controlnet comes with multiple auxiliary models, each which allows a different type of conditioning

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.

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.

Some of the additional conditionings can be extracted from images via additional models. We extracted these additional models from the original controlnet repo into a separate package that can be found on github.

Canny edge detection

Install opencv

$ pip install opencv-contrib-python

import cv2
from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
import numpy as np

image = Image.open('images/bird.png')
image = np.array(image)

low_threshold = 100
high_threshold = 200

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)

controlnet = ControlNetModel.from_pretrained(
    "fusing/stable-diffusion-v1-5-controlnet-canny",
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
)
pipe.to('cuda')

image = pipe("bird", image).images[0]

image.save('images/bird_canny_out.png')

bird

bird_canny

bird_canny_out

M-LSD Straight line detection

Install the additional controlnet models package.

$ pip install git+https://github.com/patrickvonplaten/human_pose.git

from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from human_pose import MLSDdetector

mlsd = MLSDdetector.from_pretrained('lllyasviel/ControlNet')

image = Image.open('images/room.png')

image = mlsd(image)

controlnet = ControlNetModel.from_pretrained(
    "fusing/stable-diffusion-v1-5-controlnet-mlsd",
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
)
pipe.to('cuda')

image = pipe("room", image).images[0]

image.save('images/room_mlsd_out.png')

room

room_mlsd

room_mlsd_out

Pose estimation

Install the additional controlnet models package.

$ pip install git+https://github.com/patrickvonplaten/human_pose.git

from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from human_pose import OpenposeDetector

openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')

image = Image.open('images/pose.png')

image = openpose(image)

controlnet = ControlNetModel.from_pretrained(
    "fusing/stable-diffusion-v1-5-controlnet-openpose",
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
)
pipe.to('cuda')

image = pipe("chef in the kitchen", image).images[0]

image.save('images/chef_pose_out.png')

pose

openpose

chef_pose_out

Semantic Segmentation

Semantic segmentation relies on transformers. Transformers is a dependency of diffusers for running controlnet, so you should have it installed already.

from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
from PIL import Image
import numpy as np
from controlnet_utils import ade_palette
import torch
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel

image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")

image = Image.open("./images/house.png").convert('RGB')

pixel_values = image_processor(image, return_tensors="pt").pixel_values

with torch.no_grad():
  outputs = image_segmentor(pixel_values)

seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]

color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3

palette = np.array(ade_palette())

for label, color in enumerate(palette):
    color_seg[seg == label, :] = color

color_seg = color_seg.astype(np.uint8)

image = Image.fromarray(color_seg)

controlnet = ControlNetModel.from_pretrained(
    "fusing/stable-diffusion-v1-5-controlnet-seg",
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
)
pipe.to('cuda')

image = pipe("house", image).images[0]

image.save('./images/house_seg_out.png')

house

house_seg

house_seg_out

Depth control

Depth control relies on transformers. Transformers is a dependency of diffusers for running controlnet, so you should have it installed already.

from transformers import pipeline
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from PIL import Image
import numpy as np

depth_estimator = pipeline('depth-estimation')

image = Image.open('./images/stormtrooper.png')
image = depth_estimator(image)['depth']
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)

controlnet = ControlNetModel.from_pretrained(
    "fusing/stable-diffusion-v1-5-controlnet-depth",
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
)
pipe.to('cuda')

image = pipe("Stormtrooper's lecture", image).images[0]

image.save('./images/stormtrooper_depth_out.png')

stormtrooper

stormtrooler_depth

stormtrooler_depth_out

Normal map 

from PIL import Image
from transformers import pipeline
import numpy as np
import cv2
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel

image = Image.open("images/toy.png").convert("RGB")

depth_estimator = pipeline("depth-estimation", model ="Intel/dpt-hybrid-midas" )

image = depth_estimator(image)['predicted_depth'][0]

image = image.numpy()

image_depth = image.copy()
image_depth -= np.min(image_depth)
image_depth /= np.max(image_depth)

bg_threhold = 0.4

x = cv2.Sobel(image, cv2.CV_32F, 1, 0, ksize=3)
x[image_depth < bg_threhold] = 0

y = cv2.Sobel(image, cv2.CV_32F, 0, 1, ksize=3)
y[image_depth < bg_threhold] = 0

z = np.ones_like(x) * np.pi * 2.0

image = np.stack([x, y, z], axis=2)
image /= np.sum(image ** 2.0, axis=2, keepdims=True) ** 0.5
image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
image = Image.fromarray(image)

controlnet = ControlNetModel.from_pretrained(
    "fusing/stable-diffusion-v1-5-controlnet-normal",
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
)
pipe.to('cuda')

image = pipe("cute toy", image).images[0]

image.save('images/toy_normal_out.png')

toy

toy_normal

toy_normal_out

Scribble

Install the additional controlnet models package.

$ pip install git+https://github.com/patrickvonplaten/human_pose.git

from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from human_pose import HEDdetector

hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')

image = Image.open('images/bag.png')

image = hed(image, scribble=True)

controlnet = ControlNetModel.from_pretrained(
    "fusing/stable-diffusion-v1-5-controlnet-scribble",
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
)
pipe.to('cuda')

image = pipe("bag", image).images[0]

image.save('images/bag_scribble_out.png')

bag

bag_scribble

bag_scribble_out

HED Boundary

Install the additional controlnet models package.

$ pip install git+https://github.com/patrickvonplaten/human_pose.git

from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from human_pose import HEDdetector

hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')

image = Image.open('images/man.png')

image = hed(image)

controlnet = ControlNetModel.from_pretrained(
    "fusing/stable-diffusion-v1-5-controlnet-hed",
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
)
pipe.to('cuda')

image = pipe("oil painting of handsome old man, masterpiece", image).images[0]

image.save('images/man_hed_out.png')

man

man_hed

man_hed_out