|
|
|
|
|
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) |
|
|
