mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
117 lines
3.8 KiB
Python
117 lines
3.8 KiB
Python
import os
|
|
import sys
|
|
import traceback
|
|
|
|
import facexlib
|
|
import gfpgan
|
|
|
|
import modules.face_restoration
|
|
from modules import paths, shared, devices, modelloader
|
|
|
|
model_dir = "GFPGAN"
|
|
user_path = None
|
|
model_path = os.path.join(paths.models_path, model_dir)
|
|
model_url = "https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth"
|
|
have_gfpgan = False
|
|
loaded_gfpgan_model = None
|
|
|
|
|
|
def gfpgann():
|
|
global loaded_gfpgan_model
|
|
global model_path
|
|
if loaded_gfpgan_model is not None:
|
|
loaded_gfpgan_model.gfpgan.to(devices.device_gfpgan)
|
|
return loaded_gfpgan_model
|
|
|
|
if gfpgan_constructor is None:
|
|
return None
|
|
|
|
models = modelloader.load_models(model_path, model_url, user_path, ext_filter="GFPGAN")
|
|
if len(models) == 1 and "http" in models[0]:
|
|
model_file = models[0]
|
|
elif len(models) != 0:
|
|
latest_file = max(models, key=os.path.getctime)
|
|
model_file = latest_file
|
|
else:
|
|
print("Unable to load gfpgan model!")
|
|
return None
|
|
if hasattr(facexlib.detection.retinaface, 'device'):
|
|
facexlib.detection.retinaface.device = devices.device_gfpgan
|
|
model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
|
|
loaded_gfpgan_model = model
|
|
|
|
return model
|
|
|
|
|
|
def send_model_to(model, device):
|
|
model.gfpgan.to(device)
|
|
model.face_helper.face_det.to(device)
|
|
model.face_helper.face_parse.to(device)
|
|
|
|
|
|
def gfpgan_fix_faces(np_image):
|
|
model = gfpgann()
|
|
if model is None:
|
|
return np_image
|
|
|
|
send_model_to(model, devices.device_gfpgan)
|
|
|
|
np_image_bgr = np_image[:, :, ::-1]
|
|
cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
|
|
np_image = gfpgan_output_bgr[:, :, ::-1]
|
|
|
|
model.face_helper.clean_all()
|
|
|
|
if shared.opts.face_restoration_unload:
|
|
send_model_to(model, devices.cpu)
|
|
|
|
return np_image
|
|
|
|
|
|
gfpgan_constructor = None
|
|
|
|
|
|
def setup_model(dirname):
|
|
global model_path
|
|
if not os.path.exists(model_path):
|
|
os.makedirs(model_path)
|
|
|
|
try:
|
|
from gfpgan import GFPGANer
|
|
from facexlib import detection, parsing
|
|
global user_path
|
|
global have_gfpgan
|
|
global gfpgan_constructor
|
|
|
|
load_file_from_url_orig = gfpgan.utils.load_file_from_url
|
|
facex_load_file_from_url_orig = facexlib.detection.load_file_from_url
|
|
facex_load_file_from_url_orig2 = facexlib.parsing.load_file_from_url
|
|
|
|
def my_load_file_from_url(**kwargs):
|
|
return load_file_from_url_orig(**dict(kwargs, model_dir=model_path))
|
|
|
|
def facex_load_file_from_url(**kwargs):
|
|
return facex_load_file_from_url_orig(**dict(kwargs, save_dir=model_path, model_dir=None))
|
|
|
|
def facex_load_file_from_url2(**kwargs):
|
|
return facex_load_file_from_url_orig2(**dict(kwargs, save_dir=model_path, model_dir=None))
|
|
|
|
gfpgan.utils.load_file_from_url = my_load_file_from_url
|
|
facexlib.detection.load_file_from_url = facex_load_file_from_url
|
|
facexlib.parsing.load_file_from_url = facex_load_file_from_url2
|
|
user_path = dirname
|
|
have_gfpgan = True
|
|
gfpgan_constructor = GFPGANer
|
|
|
|
class FaceRestorerGFPGAN(modules.face_restoration.FaceRestoration):
|
|
def name(self):
|
|
return "GFPGAN"
|
|
|
|
def restore(self, np_image):
|
|
return gfpgan_fix_faces(np_image)
|
|
|
|
shared.face_restorers.append(FaceRestorerGFPGAN())
|
|
except Exception:
|
|
print("Error setting up GFPGAN:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|