mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
86 lines
2.8 KiB
Python
86 lines
2.8 KiB
Python
import os
|
|
import sys
|
|
import warnings
|
|
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image
|
|
|
|
import invokeai.backend.util.logging as logger
|
|
from invokeai.backend.globals import Globals
|
|
|
|
class GFPGAN:
|
|
def __init__(self, gfpgan_model_path="models/gfpgan/GFPGANv1.4.pth") -> None:
|
|
if not os.path.isabs(gfpgan_model_path):
|
|
gfpgan_model_path = os.path.abspath(
|
|
os.path.join(Globals.root, gfpgan_model_path)
|
|
)
|
|
self.model_path = gfpgan_model_path
|
|
self.gfpgan_model_exists = os.path.isfile(self.model_path)
|
|
|
|
if not self.gfpgan_model_exists:
|
|
logger.error("NOT FOUND: GFPGAN model not found at " + self.model_path)
|
|
return None
|
|
|
|
def model_exists(self):
|
|
return os.path.isfile(self.model_path)
|
|
|
|
def process(self, image, strength: float, seed: str = None):
|
|
if seed is not None:
|
|
logger.info(f"GFPGAN - Restoring Faces for image seed:{seed}")
|
|
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("ignore", category=DeprecationWarning)
|
|
warnings.filterwarnings("ignore", category=UserWarning)
|
|
cwd = os.getcwd()
|
|
os.chdir(os.path.join(Globals.root, "models"))
|
|
try:
|
|
from gfpgan import GFPGANer
|
|
|
|
self.gfpgan = GFPGANer(
|
|
model_path=self.model_path,
|
|
upscale=1,
|
|
arch="clean",
|
|
channel_multiplier=2,
|
|
bg_upsampler=None,
|
|
)
|
|
except Exception:
|
|
import traceback
|
|
|
|
logger.error("Error loading GFPGAN:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
os.chdir(cwd)
|
|
|
|
if self.gfpgan is None:
|
|
logger.warning("WARNING: GFPGAN not initialized.")
|
|
logger.warning(
|
|
f"Download https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth to {self.model_path}"
|
|
)
|
|
|
|
image = image.convert("RGB")
|
|
|
|
# GFPGAN expects a BGR np array; make array and flip channels
|
|
bgr_image_array = np.array(image, dtype=np.uint8)[..., ::-1]
|
|
|
|
_, _, restored_img = self.gfpgan.enhance(
|
|
bgr_image_array,
|
|
has_aligned=False,
|
|
only_center_face=False,
|
|
paste_back=True,
|
|
)
|
|
|
|
# Flip the channels back to RGB
|
|
res = Image.fromarray(restored_img[..., ::-1])
|
|
|
|
if strength < 1.0:
|
|
# Resize the image to the new image if the sizes have changed
|
|
if restored_img.size != image.size:
|
|
image = image.resize(res.size)
|
|
res = Image.blend(image, res, strength)
|
|
|
|
if torch.cuda.is_available():
|
|
torch.cuda.empty_cache()
|
|
self.gfpgan = None
|
|
|
|
return res
|