| from __future__ import print_function |
|
|
| import os |
| import cv2 |
| import numpy as np |
| import torch |
| from einops import rearrange |
|
|
| from modules import devices |
| from modules.safe import unsafe_torch_load |
| from annotator.teed.ted import TED |
| from annotator.util import load_model, safe_step |
| from annotator.annotator_path import models_path |
|
|
|
|
| class TEEDDetector: |
| """https://github.com/xavysp/TEED""" |
|
|
| model_dir = os.path.join(models_path, "TEED") |
|
|
| def __init__(self, mteed: bool = False): |
| self.device = devices.get_device_for("controlnet") |
| self.model = TED().to(self.device).eval() |
|
|
| if mteed: |
| self.load_mteed_model() |
| else: |
| self.load_teed_model() |
|
|
| def load_teed_model(self): |
| """Load vanilla TEED model""" |
| remote_url = os.environ.get( |
| "CONTROLNET_TEED_MODEL_URL", |
| "https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/resolve/main/Annotators/7_model.pth", |
| ) |
| model_path = load_model( |
| "7_model.pth", remote_url=remote_url, model_dir=self.model_dir |
| ) |
| self.model.load_state_dict(unsafe_torch_load(model_path)) |
|
|
| def load_mteed_model(self): |
| """Load MTEED model for Anyline""" |
| remote_url = ( |
| "https://huggingface.co/TheMistoAI/MistoLine/resolve/main/Anyline/MTEED.pth" |
| ) |
| model_path = load_model( |
| "MTEED.pth", remote_url=remote_url, model_dir=self.model_dir |
| ) |
| self.model.load_state_dict(unsafe_torch_load(model_path)) |
|
|
| def unload_model(self): |
| if self.model is not None: |
| self.model.cpu() |
|
|
| def __call__(self, image: np.ndarray, safe_steps: int = 2) -> np.ndarray: |
|
|
| self.model.to(self.device) |
|
|
| H, W, _ = image.shape |
| with torch.no_grad(): |
| image_teed = torch.from_numpy(image.copy()).float().to(self.device) |
| image_teed = rearrange(image_teed, "h w c -> 1 c h w") |
| edges = self.model(image_teed) |
| edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] |
| edges = [ |
| cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges |
| ] |
| edges = np.stack(edges, axis=2) |
| edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) |
| if safe_steps != 0: |
| edge = safe_step(edge, safe_steps) |
| edge = (edge * 255.0).clip(0, 255).astype(np.uint8) |
| return edge |
|
|
