121 lines
3.7 KiB
Python
121 lines
3.7 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 ..onnx import OnnxRRDBNet
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from ..params import DeviceParams, ImageParams, StageParams, UpscaleParams
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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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TAG_X4_V3 = "real-esrgan-x4-v3"
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class UpscaleRealESRGANStage(BaseStage):
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def load(
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self, server: ServerContext, params: UpscaleParams, device: DeviceParams, tile=0
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):
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# must be within load function for patches to take effect
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# TODO: rewrite and remove
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from realesrgan import RealESRGANer
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class RealESRGANWrapper(RealESRGANer):
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def __init__(
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self,
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scale,
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model_path,
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dni_weight=None,
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model=None,
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tile=0,
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tile_pad=10,
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pre_pad=10,
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half=False,
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device=None,
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gpu_id=None,
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):
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self.scale = scale
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self.tile_size = tile
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self.tile_pad = tile_pad
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self.pre_pad = pre_pad
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self.mod_scale = None
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self.half = half
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self.model = model
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self.device = device
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model_file = f"{params.upscale_model}.onnx"
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model_path = path.join(server.model_path, model_file)
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cache_key = (model_path, params.scale)
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cache_pipe = server.cache.get(ModelTypes.upscaling, cache_key)
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if cache_pipe is not None:
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logger.info("reusing existing Real ESRGAN pipeline")
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return cache_pipe
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if not path.isfile(model_path):
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raise FileNotFoundError("Real ESRGAN model not found at %s" % model_path)
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# TODO: swap for regular RRDBNet after rewriting wrapper
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model = OnnxRRDBNet(
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server,
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model_file,
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provider=device.ort_provider(),
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sess_options=device.sess_options(),
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)
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dni_weight = None
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if params.upscale_model == TAG_X4_V3 and params.denoise != 1:
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wdn_model_path = model_path.replace(TAG_X4_V3, "%s-wdn" % TAG_X4_V3)
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model_path = [model_path, wdn_model_path]
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dni_weight = [params.denoise, 1 - params.denoise]
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logger.debug("loading Real ESRGAN upscale model from %s", model_path)
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upsampler = RealESRGANWrapper(
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scale=params.scale,
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model_path=None,
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dni_weight=dni_weight,
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model=model,
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tile=tile,
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tile_pad=params.tile_pad,
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pre_pad=params.pre_pad,
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half=("torch-fp16" in server.optimizations),
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device=device.torch_str(),
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)
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server.cache.set(ModelTypes.upscaling, cache_key, upsampler)
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run_gc([device])
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return upsampler
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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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stage_source: Optional[Image.Image] = None,
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**kwargs,
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) -> StageResult:
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logger.info("upscaling image with Real ESRGAN: x%s", upscale.scale)
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upsampler = self.load(
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server, upscale, worker.get_device(), tile=stage.tile_size
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)
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outputs = []
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for source in sources.as_arrays():
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output, _ = upsampler.enhance(source, outscale=upscale.outscale)
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logger.info("final output image size: %s", output.shape)
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outputs.append(output)
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return StageResult(arrays=outputs, metadata=sources.metadata)
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