imaginAIry/imaginairy/enhancers/face_restoration_codeformer.py
2024-01-14 16:50:17 -08:00

160 lines
4.7 KiB
Python

"""Code for enhancing facial images"""
import logging
from functools import lru_cache
import numpy as np
import torch
from PIL import Image
from torchvision.transforms.functional import normalize
from imaginairy.utils.downloads import get_cached_url_path
from imaginairy.vendored.basicsr.img_util import img2tensor, tensor2img
from imaginairy.vendored.codeformer.codeformer_arch import CodeFormer
from imaginairy.vendored.facexlib.utils.face_restoration_helper import FaceRestoreHelper
logger = logging.getLogger(__name__)
face_restore_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
half_mode = face_restore_device == "cuda"
def load_file_from_url(
url, model_dir=None, progress=True, file_name=None, save_dir=None
):
return get_cached_url_path(url, category="facexlib")
@lru_cache(maxsize=1)
def patch_download_function_in_facexlib_modules():
"""Replaces the custom weights downloaded with the standard imaginairy one."""
import imaginairy.vendored.facexlib.utils.misc
from imaginairy.vendored.facexlib import (
alignment,
assessment,
detection,
headpose,
matting,
parsing,
recognition,
tracking,
visualization,
)
modules = [
alignment,
assessment,
detection,
headpose,
matting,
parsing,
recognition,
tracking,
visualization,
imaginairy.vendored.facexlib.utils.misc,
]
for m in modules:
m.load_file_from_url = load_file_from_url
patch_download_function_in_facexlib_modules()
@lru_cache
def codeformer_model():
model = CodeFormer(
dim_embd=512,
codebook_size=1024,
n_head=8,
n_layers=9,
connect_list=["32", "64", "128", "256"],
).to(face_restore_device)
url = "https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth"
ckpt_path = get_cached_url_path(url)
checkpoint = torch.load(ckpt_path)["params_ema"]
model.load_state_dict(checkpoint)
model.eval()
if half_mode:
model = model.half()
return model
@lru_cache
def face_restore_helper():
"""
Provide a singleton of FaceRestoreHelper.
FaceRestoreHelper loads a model internally so we need to cache it
or we end up with a memory leak
"""
face_helper = FaceRestoreHelper(
upscale_factor=1,
face_size=512,
crop_ratio=(1, 1),
det_model="retinaface_resnet50",
save_ext="png",
use_parse=True,
device=face_restore_device,
)
return face_helper
def enhance_faces(img, fidelity=0):
net = codeformer_model()
face_helper = face_restore_helper()
face_helper.clean_all()
image = img.convert("RGB")
np_img = np.array(image, dtype=np.uint8)
# rotate to BGR
np_img = np_img[:, :, ::-1]
face_helper.read_image(np_img)
# get face landmarks for each face
num_det_faces = face_helper.get_face_landmarks_5(
only_center_face=False, resize=640, eye_dist_threshold=5
)
logger.debug(f"Enhancing {num_det_faces} faces")
# align and warp each face
face_helper.align_warp_face()
# face restoration for each cropped face
for face_box, cropped_face in zip(face_helper.det_faces, face_helper.cropped_faces):
x1, y1, x2, y2, scaling = face_box
face_width = x2 - x1
face_height = y2 - y1
logger.debug(f"Face detected. size: {face_width:1f}x{face_height:.1f}")
if face_width > 512 or face_height > 512:
logger.debug(
f"Face too large: ({face_width:.1f}x{face_height:.1f}). skipping enhancement"
)
face_helper.add_restored_face(cropped_face)
continue
# prepare data
cropped_face_t = img2tensor(cropped_face / 255.0, 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(face_restore_device)
try:
with torch.no_grad():
output = net(cropped_face_t, w=fidelity, adain=True)[0]
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
torch.cuda.empty_cache()
except Exception as error:
logger.exception(f"\tFailed inference for CodeFormer: {error}")
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype("uint8")
face_helper.add_restored_face(restored_face)
face_helper.get_inverse_affine(None)
# paste each restored face to the input image
restored_img = face_helper.paste_faces_to_input_image()
res = Image.fromarray(restored_img[:, :, ::-1])
face_helper.clean_all()
return res