70 lines
2.1 KiB
Python
70 lines
2.1 KiB
Python
from logging import getLogger
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from PIL import Image
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from ..chain import (
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ChainPipeline,
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correct_codeformer,
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correct_gfpgan,
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upscale_resrgan,
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upscale_stable_diffusion,
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)
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from ..params import ImageParams, SizeChart, StageParams, UpscaleParams
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from ..utils import ServerContext
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from .device_pool import JobContext, ProgressCallback
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logger = getLogger(__name__)
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def run_upscale_correction(
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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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image: Image.Image,
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*,
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upscale: UpscaleParams,
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callback: ProgressCallback = None,
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) -> Image.Image:
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"""
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This is a convenience method for a chain pipeline that will run upscaling and
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correction, based on the `upscale` params.
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"""
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logger.info("running upscaling and correction pipeline")
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chain = ChainPipeline()
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if upscale.scale > 1:
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if "esrgan" in upscale.upscale_model:
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esrgan_stage = StageParams(
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tile_size=stage.tile_size, outscale=upscale.outscale
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)
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chain.append((upscale_resrgan, esrgan_stage, None))
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elif "stable-diffusion" in upscale.upscale_model:
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mini_tile = min(SizeChart.mini, stage.tile_size)
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sd_stage = StageParams(tile_size=mini_tile, outscale=upscale.outscale)
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chain.append((upscale_stable_diffusion, sd_stage, None))
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else:
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logger.warn("unknown upscaling model: %s", upscale.upscale_model)
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if upscale.faces:
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face_stage = StageParams(
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tile_size=stage.tile_size, outscale=upscale.face_outscale
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)
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if "codeformer" in upscale.correction_model:
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chain.append((correct_codeformer, face_stage, None))
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elif "gfpgan" in upscale.correction_model:
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chain.append((correct_gfpgan, face_stage, None))
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else:
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logger.warn("unknown correction model: %s", upscale.correction_model)
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return chain(
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job,
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server,
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params,
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image,
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prompt=params.prompt,
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upscale=upscale,
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callback=callback,
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)
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