82 lines
2.0 KiB
Python
82 lines
2.0 KiB
Python
from logging import getLogger
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from os import path
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from typing import Optional
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import numpy as np
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from gfpgan import GFPGANer
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from PIL import Image
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from ..device_pool import JobContext
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from ..params import DeviceParams, ImageParams, StageParams, UpscaleParams
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from ..utils import ServerContext, run_gc
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logger = getLogger(__name__)
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last_pipeline_instance: Optional[GFPGANer] = None
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last_pipeline_params: Optional[str] = None
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def load_gfpgan(
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server: ServerContext,
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stage: StageParams,
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upscale: UpscaleParams,
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device: DeviceParams,
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):
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global last_pipeline_instance
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global last_pipeline_params
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face_path = path.join(server.model_path, "%s.pth" % (upscale.correction_model))
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if last_pipeline_instance is not None and face_path == last_pipeline_params:
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logger.info("reusing existing GFPGAN pipeline")
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return last_pipeline_instance
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logger.debug("loading GFPGAN model from %s", face_path)
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# TODO: find a way to pass the ONNX model to underlying architectures
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gfpgan = GFPGANer(
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arch="clean",
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bg_upsampler=None,
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channel_multiplier=2,
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model_path=face_path,
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upscale=upscale.face_outscale,
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)
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last_pipeline_instance = gfpgan
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last_pipeline_params = face_path
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run_gc()
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return gfpgan
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def correct_gfpgan(
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job: JobContext,
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server: ServerContext,
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stage: StageParams,
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_params: ImageParams,
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source_image: Image.Image,
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*,
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upscale: UpscaleParams,
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**kwargs,
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) -> Image.Image:
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if upscale.correction_model is None:
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logger.warn("no face model given, skipping")
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return source_image
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logger.info("correcting faces with GFPGAN model: %s", upscale.correction_model)
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device = job.get_device()
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gfpgan = load_gfpgan(server, stage, upscale, device)
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output = np.array(source_image)
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_, _, output = gfpgan.enhance(
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output,
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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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weight=upscale.face_strength,
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)
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output = Image.fromarray(output, "RGB")
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return output
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