From b621a63cf68c788487684250856707cb352b82d0 Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Mon, 25 Dec 2023 23:01:02 +0200 Subject: Unify CodeFormer and GFPGAN restoration backends, use Spandrel for GFPGAN --- modules/codeformer_model.py | 158 +++++++++++--------------------------------- 1 file changed, 40 insertions(+), 118 deletions(-) (limited to 'modules/codeformer_model.py') diff --git a/modules/codeformer_model.py b/modules/codeformer_model.py index 517eadfd..ceda4bab 100644 --- a/modules/codeformer_model.py +++ b/modules/codeformer_model.py @@ -1,140 +1,62 @@ -import os +from __future__ import annotations + +import logging -import cv2 import torch -import modules.face_restoration -import modules.shared -from modules import shared, devices, modelloader, errors -from modules.paths import models_path +from modules import ( + devices, + errors, + face_restoration, + face_restoration_utils, + modelloader, + shared, +) + +logger = logging.getLogger(__name__) -model_dir = "Codeformer" -model_path = os.path.join(models_path, model_dir) model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth' +model_download_name = 'codeformer-v0.1.0.pth' -codeformer = None +# used by e.g. postprocessing_codeformer.py +codeformer: face_restoration.FaceRestoration | None = None -class FaceRestorerCodeFormer(modules.face_restoration.FaceRestoration): +class FaceRestorerCodeFormer(face_restoration_utils.CommonFaceRestoration): def name(self): return "CodeFormer" - def __init__(self, dirname): - self.net = None - self.face_helper = None - self.cmd_dir = dirname - - def create_models(self): - from facexlib.detection import retinaface - from facexlib.utils.face_restoration_helper import FaceRestoreHelper - - if self.net is not None and self.face_helper is not None: - self.net.to(devices.device_codeformer) - return self.net, self.face_helper - model_paths = modelloader.load_models( - model_path, - model_url, - self.cmd_dir, - download_name='codeformer-v0.1.0.pth', + def load_net(self) -> torch.Module: + for model_path in modelloader.load_models( + model_path=self.model_path, + model_url=model_url, + command_path=self.model_path, + download_name=model_download_name, ext_filter=['.pth'], - ) - - if len(model_paths) != 0: - ckpt_path = model_paths[0] - else: - print("Unable to load codeformer model.") - return None, None - net = modelloader.load_spandrel_model(ckpt_path, device=devices.device_codeformer) - - if hasattr(retinaface, 'device'): - retinaface.device = devices.device_codeformer - - face_helper = FaceRestoreHelper( - upscale_factor=1, - face_size=512, - crop_ratio=(1, 1), - det_model='retinaface_resnet50', - save_ext='png', - use_parse=True, - device=devices.device_codeformer, - ) - - self.net = net - self.face_helper = face_helper - - def send_model_to(self, device): - self.net.to(device) - self.face_helper.face_det.to(device) - self.face_helper.face_parse.to(device) - - def restore(self, np_image, w=None): - from torchvision.transforms.functional import normalize - from basicsr.utils import img2tensor, tensor2img - np_image = np_image[:, :, ::-1] - - original_resolution = np_image.shape[0:2] - - self.create_models() - if self.net is None or self.face_helper is None: - return np_image - - self.send_model_to(devices.device_codeformer) - - self.face_helper.clean_all() - self.face_helper.read_image(np_image) - self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5) - self.face_helper.align_warp_face() - - for cropped_face in self.face_helper.cropped_faces: - cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True) - normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) - cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer) - - try: - with torch.no_grad(): - res = self.net(cropped_face_t, w=w if w is not None else shared.opts.code_former_weight, adain=True) - if isinstance(res, tuple): - output = res[0] - else: - output = res - if not isinstance(res, torch.Tensor): - raise TypeError(f"Expected torch.Tensor, got {type(res)}") - restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1)) - del output - devices.torch_gc() - except Exception: - errors.report('Failed inference for CodeFormer', exc_info=True) - restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1)) - - restored_face = restored_face.astype('uint8') - self.face_helper.add_restored_face(restored_face) - - self.face_helper.get_inverse_affine(None) - - restored_img = self.face_helper.paste_faces_to_input_image() - restored_img = restored_img[:, :, ::-1] + ): + return modelloader.load_spandrel_model( + model_path, + device=devices.device_codeformer, + ).model + raise ValueError("No codeformer model found") - if original_resolution != restored_img.shape[0:2]: - restored_img = cv2.resize( - restored_img, - (0, 0), - fx=original_resolution[1]/restored_img.shape[1], - fy=original_resolution[0]/restored_img.shape[0], - interpolation=cv2.INTER_LINEAR, - ) + def get_device(self): + return devices.device_codeformer - self.face_helper.clean_all() + def restore(self, np_image, w: float | None = None): + if w is None: + w = getattr(shared.opts, "code_former_weight", 0.5) - if shared.opts.face_restoration_unload: - self.send_model_to(devices.cpu) + def restore_face(cropped_face_t): + assert self.net is not None + return self.net(cropped_face_t, w=w, adain=True)[0] - return restored_img + return self.restore_with_helper(np_image, restore_face) -def setup_model(dirname): - os.makedirs(model_path, exist_ok=True) +def setup_model(dirname: str) -> None: + global codeformer try: - global codeformer codeformer = FaceRestorerCodeFormer(dirname) shared.face_restorers.append(codeformer) except Exception: -- cgit v1.2.1