controlnet-canny-sdxl-1.0#

README(From Huggingface)#

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)

Usage#

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 paddle
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(
    "diffusers/controlnet-canny-sdxl-1.0",
    dtype=paddle.float16
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", dtype=paddle.float16)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
    "stabilityai/stable-diffusion-xl-base-1.0",
    controlnet=controlnet,
    vae=vae,
    dtype=paddle.float16,
)
pipe.enable_model_cpu_offload()

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,
    ).images

images[0].save(f"hug_lab.png")

![ images_10)

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

Training#

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.

Compute#

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#

fp16

Model Files#

README.md (3.5 KB)
config.json (1.3 KB)
model_state.pdparams (4.7 GB)

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controlnet-canny-sdxl-1.0

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