mirror of
https://github.com/brycedrennan/imaginAIry
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106 lines
3.3 KiB
Python
106 lines
3.3 KiB
Python
import logging
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from functools import lru_cache
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import numpy as np
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import torch
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from facexlib.utils.face_restoration_helper import FaceRestoreHelper
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from PIL import Image
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from torchvision.transforms.functional import normalize
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from imaginairy.model_manager import get_cached_url_path
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from imaginairy.vendored.basicsr.img_util import img2tensor, tensor2img
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from imaginairy.vendored.codeformer.codeformer_arch import CodeFormer
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logger = logging.getLogger(__name__)
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face_restore_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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half_mode = face_restore_device == "cuda"
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@lru_cache()
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def codeformer_model():
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model = CodeFormer(
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dim_embd=512,
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codebook_size=1024,
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n_head=8,
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n_layers=9,
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connect_list=["32", "64", "128", "256"],
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).to(face_restore_device)
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url = "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
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ckpt_path = get_cached_url_path(url)
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checkpoint = torch.load(ckpt_path)["params_ema"]
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model.load_state_dict(checkpoint)
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model.eval()
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if half_mode:
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model = model.half()
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return model
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@lru_cache()
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def face_restore_helper():
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"""
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Provide a singleton of FaceRestoreHelper.
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FaceRestoreHelper loads a model internally so we need to cache it
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or we end up with a memory leak
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"""
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face_helper = FaceRestoreHelper(
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upscale_factor=1,
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face_size=512,
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crop_ratio=(1, 1),
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det_model="retinaface_resnet50",
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save_ext="png",
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use_parse=True,
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device=face_restore_device,
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)
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return face_helper
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def enhance_faces(img, fidelity=0):
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net = codeformer_model()
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face_helper = face_restore_helper()
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face_helper.clean_all()
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image = img.convert("RGB")
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np_img = np.array(image, dtype=np.uint8)
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# rotate to BGR
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np_img = np_img[:, :, ::-1]
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face_helper.read_image(np_img)
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# get face landmarks for each face
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num_det_faces = face_helper.get_face_landmarks_5(
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only_center_face=False, resize=640, eye_dist_threshold=5
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)
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logger.info(f"Enhancing {num_det_faces} faces")
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# align and warp each face
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face_helper.align_warp_face()
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# face restoration for each cropped face
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for cropped_face in face_helper.cropped_faces:
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# prepare data
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cropped_face_t = img2tensor(cropped_face / 255.0, bgr2rgb=True, float32=True)
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normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
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cropped_face_t = cropped_face_t.unsqueeze(0).to(face_restore_device)
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try:
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with torch.no_grad():
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output = net(cropped_face_t, w=fidelity, adain=True)[0] # noqa
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restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
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del output
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torch.cuda.empty_cache()
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except Exception as error: # noqa
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logger.exception(f"\tFailed inference for CodeFormer: {error}")
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restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
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restored_face = restored_face.astype("uint8")
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face_helper.add_restored_face(restored_face)
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face_helper.get_inverse_affine(None)
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# paste each restored face to the input image
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restored_img = face_helper.paste_faces_to_input_image()
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res = Image.fromarray(restored_img[:, :, ::-1])
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face_helper.clean_all()
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return res
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