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Merge pull request #520 from madrang/fix-gfpgan

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Fix the gfpgan fix for multi-gpu
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cmdr2 2022-11-30 13:08:10 +05:30 committed by GitHub
commit 426f92595e
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1 changed files with 20 additions and 16 deletions
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36
ui/sd_internal/runtime.py
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@ -28,6 +28,8 @@ from gfpgan import GFPGANer
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
from threading import Lock
import uuid
logging.set_verbosity_error()
@ -35,7 +37,7 @@ logging.set_verbosity_error()
# consts
config_yaml = "optimizedSD/v1-inference.yaml"
filename_regex = re.compile('[^a-zA-Z0-9]')
force_gfpgan_to_cuda0 = True # workaround: gfpgan currently works only on cuda:0
gfpgan_temp_device_lock = Lock() # workaround: gfpgan currently can only start on one device at a time.
# api stuff
from sd_internal import device_manager
@ -309,12 +311,6 @@ def move_to_cpu(model):
def load_model_gfpgan():
if thread_data.gfpgan_file is None: raise ValueError(f'Thread gfpgan_file is undefined.')
# hack for a bug in facexlib: https://github.com/xinntao/facexlib/pull/19/files
from facexlib.detection import retinaface
retinaface.device = torch.device(thread_data.device)
print('forced retinaface.device to', thread_data.device)
model_path = thread_data.gfpgan_file + ".pth"
thread_data.model_gfpgan = GFPGANer(device=torch.device(thread_data.device), model_path=model_path, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
print('loaded', thread_data.gfpgan_file, 'to', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
@ -370,15 +366,23 @@ def apply_filters(filter_name, image_data, model_path=None):
image_data.to(thread_data.device)
if filter_name == 'gfpgan':
if model_path is not None and model_path != thread_data.gfpgan_file:
thread_data.gfpgan_file = model_path
load_model_gfpgan()
elif not thread_data.model_gfpgan:
load_model_gfpgan()
if thread_data.model_gfpgan is None: raise Exception('Model "gfpgan" not loaded.')
print('enhance with', thread_data.gfpgan_file, 'on', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
_, _, output = thread_data.model_gfpgan.enhance(image_data[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
image_data = output[:,:,::-1]
# This lock is only ever used here. No need to use timeout for the request. Should never deadlock.
with gfpgan_temp_device_lock: # Wait for any other devices to complete before starting.
# hack for a bug in facexlib: https://github.com/xinntao/facexlib/pull/19/files
from facexlib.detection import retinaface
retinaface.device = torch.device(thread_data.device)
print('forced retinaface.device to', thread_data.device)
if model_path is not None and model_path != thread_data.gfpgan_file:
thread_data.gfpgan_file = model_path
load_model_gfpgan()
elif not thread_data.model_gfpgan:
load_model_gfpgan()
if thread_data.model_gfpgan is None: raise Exception('Model "gfpgan" not loaded.')
print('enhance with', thread_data.gfpgan_file, 'on', thread_data.model_gfpgan.device, 'precision', thread_data.precision)
_, _, output = thread_data.model_gfpgan.enhance(image_data[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
image_data = output[:,:,::-1]
if filter_name == 'real_esrgan':
if model_path is not None and model_path != thread_data.real_esrgan_file: