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import os
import torch
import numpy as np
from einops import rearrange
from annotator.pidinet.model import pidinet
from annotator.util import safe_step
from modules import devices
from annotator.annotator_path import models_path
from scripts.utils import load_state_dict
netNetwork = None
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/table5_pidinet.pth"
modeldir = os.path.join(models_path, "pidinet")
old_modeldir = os.path.dirname(os.path.realpath(__file__))
def apply_pidinet(input_image, is_safe=False, apply_fliter=False):
global netNetwork
if netNetwork is None:
modelpath = os.path.join(modeldir, "table5_pidinet.pth")
old_modelpath = os.path.join(old_modeldir, "table5_pidinet.pth")
if os.path.exists(old_modelpath):
modelpath = old_modelpath
elif not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=modeldir)
netNetwork = pidinet()
ckp = load_state_dict(modelpath)
netNetwork.load_state_dict({k.replace('module.',''):v for k, v in ckp.items()})
netNetwork = netNetwork.to(devices.get_device_for("controlnet"))
netNetwork.eval()
assert input_image.ndim == 3
input_image = input_image[:, :, ::-1].copy()
with torch.no_grad():
image_pidi = torch.from_numpy(input_image).float().to(devices.get_device_for("controlnet"))
image_pidi = image_pidi / 255.0
image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w')
edge = netNetwork(image_pidi)[-1]
edge = edge.cpu().numpy()
if apply_fliter:
edge = edge > 0.5
if is_safe:
edge = safe_step(edge)
edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
return edge[0][0]
def unload_pid_model():
global netNetwork
if netNetwork is not None:
netNetwork.cpu() |