Spaces:
Paused
Paused
File size: 2,365 Bytes
3f9c56c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
# https://github.com/advimman/lama
import yaml
import torch
from omegaconf import OmegaConf
import numpy as np
from einops import rearrange
import os
from modules import devices
from annotator.annotator_path import models_path
from annotator.lama.saicinpainting.training.trainers import load_checkpoint
class LamaInpainting:
model_dir = os.path.join(models_path, "lama")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetLama.pth"
modelpath = os.path.join(self.model_dir, "ControlNetLama.pth")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml')
cfg = yaml.safe_load(open(config_path, 'rt'))
cfg = OmegaConf.create(cfg)
cfg.training_model.predict_only = True
cfg.visualizer.kind = 'noop'
self.model = load_checkpoint(cfg, os.path.abspath(modelpath), strict=False, map_location='cpu')
self.model = self.model.to(self.device)
self.model.eval()
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model()
self.model.to(self.device)
color = np.ascontiguousarray(input_image[:, :, 0:3]).astype(np.float32) / 255.0
mask = np.ascontiguousarray(input_image[:, :, 3:4]).astype(np.float32) / 255.0
with torch.no_grad():
color = torch.from_numpy(color).float().to(self.device)
mask = torch.from_numpy(mask).float().to(self.device)
mask = (mask > 0.5).float()
color = color * (1 - mask)
image_feed = torch.cat([color, mask], dim=2)
image_feed = rearrange(image_feed, 'h w c -> 1 c h w')
result = self.model(image_feed)[0]
result = rearrange(result, 'c h w -> h w c')
result = result * mask + color * (1 - mask)
result *= 255.0
return result.detach().cpu().numpy().clip(0, 255).astype(np.uint8)
|