121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
from logging import getLogger
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from os import path
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import numpy as np
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from PIL import Image
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from ..onnx import OnnxNet
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from ..params import DeviceParams, ImageParams, StageParams, UpscaleParams
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from ..server import ServerContext
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from ..utils import run_gc
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from ..worker import WorkerContext
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logger = getLogger(__name__)
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last_pipeline_instance = None
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last_pipeline_params = (None, None)
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TAG_X4_V3 = "real-esrgan-x4-v3"
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def load_resrgan(
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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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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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model_file = "%s.%s" % (params.upscale_model, params.format)
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model_path = path.join(server.model_path, model_file)
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cache_key = (model_path, params.format)
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cache_pipe = server.cache.get("resrgan", 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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if params.format == "onnx":
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# use ONNX acceleration, if available
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model = OnnxNet(
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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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elif params.format == "pth":
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if TAG_X4_V3 in model_file:
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# the x4-v3 model needs a different network
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model = SRVGGNetCompact(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_conv=32,
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upscale=4,
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act_type="prelu",
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)
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else:
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=params.scale,
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)
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else:
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raise ValueError("unknown platform %s" % params.format)
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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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# TODO: shouldn't need the PTH file
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model_path_pth = path.join(server.cache_path, ("%s.pth" % params.upscale_model))
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upsampler = RealESRGANer(
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scale=params.scale,
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model_path=model_path_pth,
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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=params.half,
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)
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server.cache.set("resrgan", cache_key, upsampler)
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run_gc([device])
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return upsampler
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def upscale_resrgan(
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job: WorkerContext,
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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,
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*,
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upscale: UpscaleParams,
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stage_source: Image.Image = None,
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**kwargs,
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) -> Image.Image:
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source = stage_source or source
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logger.info("upscaling image with Real ESRGAN: x%s", upscale.scale)
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output = np.array(source)
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upsampler = load_resrgan(server, upscale, job.get_device(), tile=stage.tile_size)
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output, _ = upsampler.enhance(output, outscale=upscale.outscale)
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output = Image.fromarray(output, "RGB")
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logger.info("final output image size: %sx%s", output.width, output.height)
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return output
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