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