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# ControlNet

[ControlNet](https://huggingface.co/papers/2302.05543) direciona um modelo de difusão pré-treinado com uma imagem de controle estrutural (bordas, profundidade ou pose) enquanto você mantém um prompt de texto. Ele congela o modelo base e adiciona uma rede paralela cujos resíduos guiam o desruidador, permitindo controle de layout sem ajuste fino.

Carregue um ControlNet para o controle de que você precisa (por exemplo, canny) e, em seguida, passe-o como `controlnet=` para [from_pretrained()](/docs/diffusers/v0.43.0/pt/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained). Use `controlnet_conditioning_scale` para definir com que intensidade o controle direciona a geração.

> [!TIP]
> ControlNets estão disponíveis para muitos modelos, como [Flux](../api/pipelines/controlnet_flux), [Hunyuan-DiT](../api/pipelines/controlnet_hunyuandit), [Stable Diffusion 3](../api/pipelines/controlnet_sd3) e outros. Os exemplos neste guia usam Flux e Stable Diffusion XL. Para T2I-Adapter em checkpoints mais antigos da família Stable Diffusion, consulte [Adaptadores legados](./legacy_adapters#t2i-adapter). Prefira ControlNet para novos trabalhos de geração controlável.

As abas abaixo mostram o ControlNet para texto-para-imagem, imagem-para-imagem e inpainting.

Gere uma imagem canny com [opencv-python](https://github.com/opencv/opencv-python).

```py
import cv2
import numpy as np
from PIL import Image
from diffusers.utils import load_image
original_image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
)
image = np.array(original_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)
canny_image = Image.fromarray(image)
```

Passe a imagem canny para o pipeline. Use o parâmetro `controlnet_conditioning_scale` para determinar quanto peso atribuir ao controle.

```py
import torch
from diffusers.utils import load_image
from diffusers import FluxControlNetPipeline, FluxControlNetModel
controlnet = FluxControlNetModel.from_pretrained(
    "InstantX/FLUX.1-dev-Controlnet-Canny", dtype=torch.bfloat16
)
pipeline = FluxControlNetPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", controlnet=controlnet, dtype=torch.bfloat16
).to("cuda")  # or "mps", "xpu", "cpu"
prompt = """
A photorealistic overhead image of a cat reclining sideways in a flamingo pool floatie holding a margarita. 
The cat is floating leisurely in the pool and completely relaxed and happy.
"""
pipeline(
    prompt, 
    control_image=canny_image,
    controlnet_conditioning_scale=0.5,
    num_inference_steps=50, 
    guidance_scale=3.5,
).images[0]
```

  
    
    imagem original
  
  
    
    imagem canny
  
  
    
    imagem gerada
  
Gere um mapa de profundidade com um pipeline de estimativa de profundidade do Transformers.

```py
import torch
import numpy as np
from PIL import Image
from transformers import DPTImageProcessor, DPTForDepthEstimation
from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, AutoencoderKL
from diffusers.utils import load_image
device = "cuda"  # or "mps", "xpu", "cpu"
depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
def get_depth_map(image):
    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
    with torch.no_grad(), torch.autocast(device):
        depth_map = depth_estimator(image).predicted_depth
    depth_map = torch.nn.functional.interpolate(
        depth_map.unsqueeze(1),
        size=(1024, 1024),
        mode="bicubic",
        align_corners=False,
    )
    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
    image = torch.cat([depth_map] * 3, dim=1)
    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
    return image
image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
).resize((1024, 1024))
depth_image = get_depth_map(image)
```

Passe o mapa de profundidade para o pipeline. Use o parâmetro `controlnet_conditioning_scale` para determinar quanto peso atribuir ao controle.

```py
controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-depth-sdxl-1.0-small",
    dtype=torch.float16,
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", dtype=torch.float16)
pipeline = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet,
    vae=vae,
    dtype=torch.float16,
).to(device)
prompt = """
A photorealistic overhead image of a cat reclining sideways in a flamingo pool floatie holding a margarita. 
The cat is floating leisurely in the pool and completely relaxed and happy.
"""
controlnet_conditioning_scale = 0.5
pipeline(
    prompt,
    image=image,
    control_image=depth_image,
    controlnet_conditioning_scale=controlnet_conditioning_scale,
    strength=0.99,
    num_inference_steps=100,
).images[0]
```

  
    
    imagem original
  
  
    
    mapa de profundidade
  
   
    
    imagem gerada
  
Gere uma imagem de máscara que marca quais pixels substituir e, em seguida, passe-a como `mask_image` junto com a imagem de controle.

```py
import cv2
import torch
import numpy as np
from PIL import Image
from diffusers.utils import load_image
from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel
init_image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
)
init_image = init_image.resize((1024, 1024))
mask_image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat_mask.png"
)
mask_image = mask_image.resize((1024, 1024))
def make_canny_condition(image):
    image = np.array(image)
    image = cv2.Canny(image, 100, 200)
    image = image[:, :, None]
    image = np.concatenate([image, image, image], axis=2)
    image = Image.fromarray(image)
    return image
control_image = make_canny_condition(init_image)
```

Passe a máscara e a imagem de controle para o pipeline. Use o parâmetro `controlnet_conditioning_scale` para determinar quanto peso atribuir ao controle.

```py
controlnet = ControlNetModel.from_pretrained(
    "diffusers/controlnet-canny-sdxl-1.0", dtype=torch.float16
)
pipeline = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, dtype=torch.float16
).to("cuda")  # or "mps", "xpu", "cpu"
pipeline(
    "a cute and fluffy bunny rabbit",
    num_inference_steps=100,
    strength=0.99,
    controlnet_conditioning_scale=0.5,
    image=init_image,
    mask_image=mask_image,
    control_image=control_image,
).images[0]
```

  
    
    imagem original
  
  
    
    imagem de máscara
  
   
    
    imagem gerada
  
## Multi-ControlNet

Compose vários ControlNets (por exemplo, canny + depth) passando-os como uma lista. Mescle as regiões sobrepostas quando possível e ajuste cada `controlnet_conditioning_scale`. Construa `canny_image` com OpenCV Canny (como em text-to-image) e `depth_image` com DPT (como em image-to-image). Redimensione todas as imagens de controle para o tamanho do pipeline antes de chamar o pipeline.

```py
import cv2
import torch
import numpy as np
from PIL import Image
from transformers import DPTImageProcessor, DPTForDepthEstimation
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
from diffusers.utils import load_image
original_image = load_image(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
)
image = np.array(original_image)
canny_image = Image.fromarray(
    np.concatenate([cv2.Canny(image, 100, 200)[:, :, None]] * 3, axis=2)
)
device = "cuda"  # or "mps", "xpu", "cpu"
depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
def get_depth_map(image):
    image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
    with torch.no_grad(), torch.autocast(device):
        depth_map = depth_estimator(image).predicted_depth
    depth_map = torch.nn.functional.interpolate(
        depth_map.unsqueeze(1),
        size=(1024, 1024),
        mode="bicubic",
        align_corners=False,
    )
    depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
    depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
    depth_map = (depth_map - depth_min) / (depth_max - depth_min)
    image = torch.cat([depth_map] * 3, dim=1)
    image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
    image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
    return image
depth_image = get_depth_map(original_image.resize((1024, 1024)))
controlnets = [
    ControlNetModel.from_pretrained(
        "diffusers/controlnet-canny-sdxl-1.0", dtype=torch.float16
    ),
    ControlNetModel.from_pretrained(
        "diffusers/controlnet-depth-sdxl-1.0-small", dtype=torch.float16
    ),
]
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", dtype=torch.float16)
pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnets, vae=vae, dtype=torch.float16
).to(device)
prompt = """
a relaxed rabbit sitting on a striped towel next to a pool with a tropical drink nearby, 
bright sunny day, vacation scene, 35mm photograph, film, professional, 4k, highly detailed
"""
negative_prompt = "lowres, bad anatomy, worst quality, low quality, deformed, ugly"
images = [canny_image.resize((1024, 1024)), depth_image.resize((1024, 1024))]
pipeline(
    prompt,
    negative_prompt=negative_prompt,
    image=images,
    num_inference_steps=100,
    controlnet_conditioning_scale=[0.5, 0.5],
).images[0]
```

  
    
    imagem canny
  
  
    
    mapa de profundidade
  
   
    
    imagem gerada
  
## Modo de suposição

[Modo de estimativa](https://github.com/lllyasviel/ControlNet/discussions/188) gera uma imagem a partir de um controle isolado quando você não tem (ou não deseja) um prompt de texto. Ele escala os resíduos do ControlNet pela profundidade do bloco, de aproximadamente `0.1` nos blocos de downblock iniciais até `1.0` no bloco intermediário. Isso impede que as camadas iniciais super-restrinjam a geração, enquanto os blocos mais profundos ainda carregam a estrutura.

```py
import torch
from diffusers.utils import load_image
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel
controlnet = ControlNetModel.from_pretrained(
  "diffusers/controlnet-canny-sdxl-1.0", dtype=torch.float16
)
pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
  "stabilityai/stable-diffusion-xl-base-1.0",
  controlnet=controlnet,
  dtype=torch.float16
).to("cuda")  # or "mps", "xpu", "cpu"
canny_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/canny-cat.png")
pipeline(
  "",
  image=canny_image,
  guess_mode=True
).images[0]
```

  
    
    imagem canny
  
  
    
    imagem gerada
  

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	# ControlNet

	[ControlNet](https://huggingface.co/papers/2302.05543) direciona um modelo de difusão pré-treinado com uma imagem de controle estrutural (bordas, profundidade ou pose) enquanto você mantém um prompt de texto. Ele congela o modelo base e adiciona uma rede paralela cujos resíduos guiam o desruidador, permitindo controle de layout sem ajuste fino.

	Carregue um ControlNet para o controle de que você precisa (por exemplo, canny) e, em seguida, passe-o como `controlnet=` para [from_pretrained()](/docs/diffusers/v0.43.0/pt/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained). Use `controlnet_conditioning_scale` para definir com que intensidade o controle direciona a geração.

	> [!TIP]
	> ControlNets estão disponíveis para muitos modelos, como [Flux](../api/pipelines/controlnet_flux), [Hunyuan-DiT](../api/pipelines/controlnet_hunyuandit), [Stable Diffusion 3](../api/pipelines/controlnet_sd3) e outros. Os exemplos neste guia usam Flux e Stable Diffusion XL. Para T2I-Adapter em checkpoints mais antigos da família Stable Diffusion, consulte [Adaptadores legados](./legacy_adapters#t2i-adapter). Prefira ControlNet para novos trabalhos de geração controlável.

	As abas abaixo mostram o ControlNet para texto-para-imagem, imagem-para-imagem e inpainting.

	Gere uma imagem canny com [opencv-python](https://github.com/opencv/opencv-python).

	```py
	import cv2
	import numpy as np
	from PIL import Image
	from diffusers.utils import load_image

	original_image = load_image(
	"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
	)

	image = np.array(original_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)
	canny_image = Image.fromarray(image)
	```

	Passe a imagem canny para o pipeline. Use o parâmetro `controlnet_conditioning_scale` para determinar quanto peso atribuir ao controle.

	```py
	import torch
	from diffusers.utils import load_image
	from diffusers import FluxControlNetPipeline, FluxControlNetModel

	controlnet = FluxControlNetModel.from_pretrained(
	"InstantX/FLUX.1-dev-Controlnet-Canny", dtype=torch.bfloat16
	)
	pipeline = FluxControlNetPipeline.from_pretrained(
	"black-forest-labs/FLUX.1-dev", controlnet=controlnet, dtype=torch.bfloat16
	).to("cuda") # or "mps", "xpu", "cpu"

	prompt = """
	A photorealistic overhead image of a cat reclining sideways in a flamingo pool floatie holding a margarita.
	The cat is floating leisurely in the pool and completely relaxed and happy.
	"""

	pipeline(
	prompt,
	control_image=canny_image,
	controlnet_conditioning_scale=0.5,
	num_inference_steps=50,
	guidance_scale=3.5,
	).images[0]
	```



	imagem original



	imagem canny



	imagem gerada


	Gere um mapa de profundidade com um pipeline de estimativa de profundidade do Transformers.

	```py
	import torch
	import numpy as np
	from PIL import Image
	from transformers import DPTImageProcessor, DPTForDepthEstimation
	from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, AutoencoderKL
	from diffusers.utils import load_image

	device = "cuda" # or "mps", "xpu", "cpu"
	depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
	feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")

	def get_depth_map(image):
	image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
	with torch.no_grad(), torch.autocast(device):
	depth_map = depth_estimator(image).predicted_depth

	depth_map = torch.nn.functional.interpolate(
	depth_map.unsqueeze(1),
	size=(1024, 1024),
	mode="bicubic",
	align_corners=False,
	)
	depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
	depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
	depth_map = (depth_map - depth_min) / (depth_max - depth_min)
	image = torch.cat([depth_map] * 3, dim=1)
	image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
	image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
	return image

	image = load_image(
	"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
	).resize((1024, 1024))
	depth_image = get_depth_map(image)
	```

	Passe o mapa de profundidade para o pipeline. Use o parâmetro `controlnet_conditioning_scale` para determinar quanto peso atribuir ao controle.

	```py
	controlnet = ControlNetModel.from_pretrained(
	"diffusers/controlnet-depth-sdxl-1.0-small",
	dtype=torch.float16,
	)
	vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", dtype=torch.float16)
	pipeline = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
	"stabilityai/stable-diffusion-xl-base-1.0",
	controlnet=controlnet,
	vae=vae,
	dtype=torch.float16,
	).to(device)

	prompt = """
	A photorealistic overhead image of a cat reclining sideways in a flamingo pool floatie holding a margarita.
	The cat is floating leisurely in the pool and completely relaxed and happy.
	"""
	controlnet_conditioning_scale = 0.5
	pipeline(
	prompt,
	image=image,
	control_image=depth_image,
	controlnet_conditioning_scale=controlnet_conditioning_scale,
	strength=0.99,
	num_inference_steps=100,
	).images[0]
	```



	imagem original



	mapa de profundidade



	imagem gerada


	Gere uma imagem de máscara que marca quais pixels substituir e, em seguida, passe-a como `mask_image` junto com a imagem de controle.

	```py
	import cv2
	import torch
	import numpy as np
	from PIL import Image
	from diffusers.utils import load_image
	from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel

	init_image = load_image(
	"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
	)
	init_image = init_image.resize((1024, 1024))
	mask_image = load_image(
	"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat_mask.png"
	)
	mask_image = mask_image.resize((1024, 1024))

	def make_canny_condition(image):
	image = np.array(image)
	image = cv2.Canny(image, 100, 200)
	image = image[:, :, None]
	image = np.concatenate([image, image, image], axis=2)
	image = Image.fromarray(image)
	return image

	control_image = make_canny_condition(init_image)
	```

	Passe a máscara e a imagem de controle para o pipeline. Use o parâmetro `controlnet_conditioning_scale` para determinar quanto peso atribuir ao controle.

	```py
	controlnet = ControlNetModel.from_pretrained(
	"diffusers/controlnet-canny-sdxl-1.0", dtype=torch.float16
	)
	pipeline = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(
	"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, dtype=torch.float16
	).to("cuda") # or "mps", "xpu", "cpu"
	pipeline(
	"a cute and fluffy bunny rabbit",
	num_inference_steps=100,
	strength=0.99,
	controlnet_conditioning_scale=0.5,
	image=init_image,
	mask_image=mask_image,
	control_image=control_image,
	).images[0]
	```



	imagem original



	imagem de máscara



	imagem gerada


	## Multi-ControlNet

	Compose vários ControlNets (por exemplo, canny + depth) passando-os como uma lista. Mescle as regiões sobrepostas quando possível e ajuste cada `controlnet_conditioning_scale`. Construa `canny_image` com OpenCV Canny (como em text-to-image) e `depth_image` com DPT (como em image-to-image). Redimensione todas as imagens de controle para o tamanho do pipeline antes de chamar o pipeline.

	```py
	import cv2
	import torch
	import numpy as np
	from PIL import Image
	from transformers import DPTImageProcessor, DPTForDepthEstimation
	from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
	from diffusers.utils import load_image

	original_image = load_image(
	"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
	)
	image = np.array(original_image)
	canny_image = Image.fromarray(
	np.concatenate([cv2.Canny(image, 100, 200)[:, :, None]] * 3, axis=2)
	)

	device = "cuda" # or "mps", "xpu", "cpu"
	depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(device)
	feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")

	def get_depth_map(image):
	image = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
	with torch.no_grad(), torch.autocast(device):
	depth_map = depth_estimator(image).predicted_depth

	depth_map = torch.nn.functional.interpolate(
	depth_map.unsqueeze(1),
	size=(1024, 1024),
	mode="bicubic",
	align_corners=False,
	)
	depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
	depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
	depth_map = (depth_map - depth_min) / (depth_max - depth_min)
	image = torch.cat([depth_map] * 3, dim=1)
	image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
	image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
	return image

	depth_image = get_depth_map(original_image.resize((1024, 1024)))

	controlnets = [
	ControlNetModel.from_pretrained(
	"diffusers/controlnet-canny-sdxl-1.0", dtype=torch.float16
	),
	ControlNetModel.from_pretrained(
	"diffusers/controlnet-depth-sdxl-1.0-small", dtype=torch.float16
	),
	]

	vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", dtype=torch.float16)
	pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
	"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnets, vae=vae, dtype=torch.float16
	).to(device)

	prompt = """
	a relaxed rabbit sitting on a striped towel next to a pool with a tropical drink nearby,
	bright sunny day, vacation scene, 35mm photograph, film, professional, 4k, highly detailed
	"""
	negative_prompt = "lowres, bad anatomy, worst quality, low quality, deformed, ugly"

	images = [canny_image.resize((1024, 1024)), depth_image.resize((1024, 1024))]

	pipeline(
	prompt,
	negative_prompt=negative_prompt,
	image=images,
	num_inference_steps=100,
	controlnet_conditioning_scale=[0.5, 0.5],
	).images[0]
	```



	imagem canny



	mapa de profundidade



	imagem gerada


	## Modo de suposição

	[Modo de estimativa](https://github.com/lllyasviel/ControlNet/discussions/188) gera uma imagem a partir de um controle isolado quando você não tem (ou não deseja) um prompt de texto. Ele escala os resíduos do ControlNet pela profundidade do bloco, de aproximadamente `0.1` nos blocos de downblock iniciais até `1.0` no bloco intermediário. Isso impede que as camadas iniciais super-restrinjam a geração, enquanto os blocos mais profundos ainda carregam a estrutura.

	```py
	import torch
	from diffusers.utils import load_image
	from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel

	controlnet = ControlNetModel.from_pretrained(
	"diffusers/controlnet-canny-sdxl-1.0", dtype=torch.float16
	)
	pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
	"stabilityai/stable-diffusion-xl-base-1.0",
	controlnet=controlnet,
	dtype=torch.float16
	).to("cuda") # or "mps", "xpu", "cpu"

	canny_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/canny-cat.png")
	pipeline(
	"",
	image=canny_image,
	guess_mode=True
	).images[0]
	```



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