95 lines
2.7 KiB
Python
95 lines
2.7 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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from PIL import Image
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from ..params import (
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DeviceParams,
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HighresParams,
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ImageParams,
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StageParams,
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UpscaleParams,
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)
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from ..server import ModelTypes, ServerContext
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from ..utils import run_gc
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from ..worker import WorkerContext
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from .base import BaseStage
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from .result import StageResult
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logger = getLogger(__name__)
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class CorrectGFPGANStage(BaseStage):
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def load(
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self,
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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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# must be within the load function for patch to take effect
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# TODO: rewrite and remove
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from gfpgan import GFPGANer
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face_path = path.join(server.cache_path, "%s.pth" % (upscale.correction_model))
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cache_key = (face_path,)
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cache_pipe = server.cache.get(ModelTypes.correction, cache_key)
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if cache_pipe is not None:
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logger.info("reusing existing GFPGAN pipeline")
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return cache_pipe
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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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device=device.torch_str(),
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model_path=face_path,
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upscale=upscale.face_outscale,
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)
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server.cache.set(ModelTypes.correction, cache_key, gfpgan)
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run_gc([device])
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return gfpgan
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def run(
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self,
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worker: WorkerContext,
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server: ServerContext,
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stage: StageParams,
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_params: ImageParams,
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sources: StageResult,
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*,
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upscale: UpscaleParams,
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highres: Optional[HighresParams] = None,
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stage_source: Optional[Image.Image] = None,
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**kwargs,
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) -> StageResult:
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upscale = upscale.with_args(**kwargs)
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if upscale.correction_model is None:
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logger.warning("no face model given, skipping")
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return sources
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logger.info("correcting faces with GFPGAN model: %s", upscale.correction_model)
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device = worker.get_device()
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gfpgan = self.load(server, stage, upscale, device)
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outputs = []
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for source in sources.as_arrays():
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cropped, restored, result = gfpgan.enhance(
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source,
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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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outputs.append(result)
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return StageResult.from_arrays(outputs, metadata=sources.metadata)
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