Update default_height_width and check_inputs functions

[kadirnar]

#3612 (comment)
I fixed the error but check it out. Because I don't know this pipeline algorithm.

import cv2
import torch
import numpy as np
from PIL import Image
from diffusers import UniPCMultistepScheduler
from diffusers.utils import load_image

input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")

# get canny image
image = cv2.Canny(np.array(input_image), 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)

controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
       "runwayml/stable-diffusion-v1-5",
       controlnet=controlnet,
       safety_checker=None,
       torch_dtype=torch.float16
       ).to('cuda:0')

pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

result_img = pipe(ref_image=input_image,
      prompt="1girl",
      image=canny_image,
      num_inference_steps=20,
      reference_attn=True,
      reference_adain=True).images[0]

[kadirnar]

Can you check? @okotaku

[HuggingFaceDocBuilderDev]

The documentation is not available anymore as the PR was closed or merged.

[patrickvonplaten]

Looks good to me

[okotaku]

@kadirnar Thank you for your contribution, but I fixed it in this PR.
#3723

[kadirnar]

Looks good to me

Should I close this pull request?

[okotaku]

Yes. My PR was merged.