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