#
ControlNet

ControlNet is an adapter that enables controllable generation such as generating an image of a cat in a specific pose or following the lines in a sketch of a specific cat. It works by adding a smaller network of “zero convolution” layers and progressively training these to avoid disrupting with the original model. The original model parameters are frozen to avoid retraining it.

A ControlNet is conditioned on extra visual information or “structural controls” (canny edge, depth maps, human pose, etc.) that can be combined with text prompts to generate images that are guided by the visual input.

> ControlNets are available to many models such as Flux, Hunyuan-DiT, Stable Diffusion 3, and more. The examples in this guide use Flux and Stable Diffusion XL.

Load a ControlNet conditioned on a specific control, such as canny edge, and pass it to the pipeline in from_pretrained().

text-to-image
image-to-image
inpainting

Generate a canny image with opencv-python.

    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)

Pass the canny image to the pipeline. Use the `controlnet_conditioning_scale` parameter to determine how much weight to assign to the control.

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

    controlnet = FluxControlNetModel.from_pretrained(
        "InstantX/FLUX.1-dev-Controlnet-Canny", torch_dtype=torch.bfloat16
    )
    pipeline = FluxControlNetPipeline.from_pretrained(
        "black-forest-labs/FLUX.1-dev", controlnet=controlnet, torch_dtype=torch.bfloat16
    ).to("cuda")

    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]

original image
canny image
generated image

##
Multi-ControlNet

You can compose multiple ControlNet conditionings, such as canny image and a depth map, to create a MultiControlNet. For the best rersults, you should mask conditionings so they don’t overlap and experiment with different `controlnet_conditioning_scale` parameters to adjust how much weight is assigned to each control input.

The example below composes a canny image and depth map.

Pass the ControlNets as a list to the pipeline and resize the images to the expected input size.

    import torch
    from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL

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

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

    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],
        strength=0.7,
    ).images[0]

canny image
depth map
generated image

##
guess_mode

Guess mode generates an image from only the control input (canny edge, depth map, pose, etc.) and without guidance from a prompt. It adjusts the scale of the ControlNet’s output residuals by a fixed ratio depending on block depth. The earlier `DownBlock` is only scaled by `0.1` and the `MidBlock` is fully scaled by `1.0`.

    import torch
    from diffusers.utils import load_iamge
    from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel

    controlnet = ControlNetModel.from_pretrained(
      "diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
    )
    pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
      "stabilityai/stable-diffusion-xl-base-1.0",
      controlnet=controlnet,
      torch_dtype=torch.float16
    ).to("cuda")

    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]

canny image
generated image
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