102 lines
3.1 KiB
Python
102 lines
3.1 KiB
Python
from logging import getLogger
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from os import path
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from typing import List, Optional
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import numpy as np
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from PIL import Image
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from ..models.onnx import OnnxModel
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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 .stage import BaseStage
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logger = getLogger(__name__)
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class UpscaleSwinIRStage(BaseStage):
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max_tile = 64
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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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model_path = path.join(server.model_path, "%s.onnx" % (upscale.upscale_model))
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cache_key = (model_path,)
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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 SwinIR pipeline")
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return cache_pipe
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logger.debug("loading SwinIR model from %s", model_path)
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pipe = OnnxModel(
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server,
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model_path,
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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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server.cache.set(ModelTypes.upscaling, cache_key, pipe)
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run_gc([device])
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return pipe
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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: List[Image.Image],
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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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) -> List[Image.Image]:
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upscale = upscale.with_args(**kwargs)
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if upscale.upscale_model is None:
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logger.warning("no correction model given, skipping")
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return sources
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logger.info("correcting faces with SwinIR model: %s", upscale.upscale_model)
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device = worker.get_device()
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swinir = self.load(server, stage, upscale, device)
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outputs = []
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for source in sources:
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# TODO: add support for grayscale (1-channel) images
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image = np.array(source) / 255.0
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image = image[:, :, [2, 1, 0]].astype(np.float32).transpose((2, 0, 1))
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image = np.expand_dims(image, axis=0)
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logger.trace("SwinIR input shape: %s", image.shape)
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scale = upscale.outscale
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dest = np.zeros(
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(
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image.shape[0],
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image.shape[1],
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image.shape[2] * scale,
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image.shape[3] * scale,
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)
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)
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logger.trace("SwinIR output shape: %s", dest.shape)
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dest = swinir(image)
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dest = np.clip(np.squeeze(dest, axis=0), 0, 1)
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dest = dest[[2, 1, 0], :, :].transpose((1, 2, 0))
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dest = (dest * 255.0).round().astype(np.uint8)
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output = Image.fromarray(dest, "RGB")
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logger.info("output image size: %s x %s", output.width, output.height)
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outputs.append(output)
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return outputs
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