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SDXL-controlnet: Canny

These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with canny conditioning. You can find some example images in the following.

prompt: a couple watching a romantic sunset, 4k photo images_0)

prompt: ultrarealistic shot of a furry blue bird images_1)

prompt: a woman, close up, detailed, beautiful, street photography, photorealistic, detailed, Kodak ektar 100, natural, candid shot images_2)

prompt: Cinematic, neoclassical table in the living room, cinematic, contour, lighting, highly detailed, winter, golden hour images_3)

prompt: a tornado hitting grass field, 1980's film grain. overcast, muted colors. images_0)


Make sure to first install the libraries:

pip install accelerate transformers safetensors opencv-python diffusers

And then we're ready to go:

from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
from diffusers.utils import load_image
from PIL import Image
import torch
import numpy as np
import cv2

prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
negative_prompt = 'low quality, bad quality, sketches'

image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png")

controlnet_conditioning_scale = 0.5  # recommended for good generalization

controlnet = ControlNetModel.from_pretrained(
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(

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)

images = pipe(
    prompt, negative_prompt=negative_prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale,



To more details, check out the official documentation of StableDiffusionXLControlNetPipeline.


Our training script was built on top of the official training script that we provide here.

Training data

This checkpoint was first trained for 20,000 steps on laion 6a resized to a max minimum dimension of 384. It was then further trained for 20,000 steps on laion 6a resized to a max minimum dimension of 1024 and then filtered to contain only minimum 1024 images. We found the further high resolution finetuning was necessary for image quality.


one 8xA100 machine

Batch size

Data parallel with a single gpu batch size of 8 for a total batch size of 64.

Hyper Parameters

Constant learning rate of 1e-4 scaled by batch size for total learning rate of 64e-4

Mixed precision


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