127 lines
4.2 KiB
Python
127 lines
4.2 KiB
Python
from logging import getLogger
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from typing import List, Optional, Tuple
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from ..params import ImageParams, SizeChart, StageParams, UpscaleParams
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from . import ChainPipeline, PipelineStage
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from .correct_codeformer import CorrectCodeformerStage
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from .correct_gfpgan import CorrectGFPGANStage
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from .upscale_bsrgan import UpscaleBSRGANStage
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from .upscale_resrgan import UpscaleRealESRGANStage
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from .upscale_stable_diffusion import UpscaleStableDiffusionStage
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from .upscale_swinir import UpscaleSwinIRStage
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logger = getLogger(__name__)
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def split_upscale(
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upscale: UpscaleParams,
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) -> Tuple[Optional[UpscaleParams], UpscaleParams]:
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if upscale.faces and (
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upscale.upscale_order == "correction-both"
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or upscale.upscale_order == "correction-first"
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):
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return (
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upscale.with_args(
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scale=1,
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outscale=1,
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),
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upscale.with_args(
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upscale_order="correction-last",
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),
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)
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else:
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return (
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None,
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upscale,
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)
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def stage_upscale_correction(
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stage: StageParams,
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params: ImageParams,
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*,
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upscale: UpscaleParams,
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chain: Optional[ChainPipeline] = None,
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pre_stages: List[PipelineStage] = None,
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post_stages: List[PipelineStage] = None,
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**kwargs,
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) -> ChainPipeline:
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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(
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"staging upscaling and correction pipeline at %s:%s",
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upscale.scale,
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upscale.outscale,
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)
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if chain is None:
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chain = ChainPipeline()
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if pre_stages is not None:
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for stage, pre_params, pre_opts in pre_stages:
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chain.append((stage, pre_params, pre_opts))
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upscale_opts = {
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**kwargs,
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"upscale": upscale,
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}
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upscale_stage = None
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if upscale.scale > 1:
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if "bsrgan" in upscale.upscale_model:
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bsrgan_params = StageParams(
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tile_size=stage.tile_size,
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outscale=upscale.outscale,
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)
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upscale_stage = (UpscaleBSRGANStage(), bsrgan_params, upscale_opts)
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elif "esrgan" in upscale.upscale_model:
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esrgan_params = StageParams(
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tile_size=stage.tile_size,
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outscale=upscale.outscale,
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)
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upscale_stage = (UpscaleRealESRGANStage(), esrgan_params, upscale_opts)
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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_params = StageParams(tile_size=mini_tile, outscale=upscale.outscale)
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upscale_stage = (UpscaleStableDiffusionStage(), sd_params, upscale_opts)
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elif "swinir" in upscale.upscale_model:
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swinir_params = StageParams(
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tile_size=stage.tile_size,
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outscale=upscale.outscale,
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)
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upscale_stage = (UpscaleSwinIRStage(), swinir_params, upscale_opts)
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else:
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logger.warn("unknown upscaling model: %s", upscale.upscale_model)
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correct_stage = None
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if upscale.faces:
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face_params = 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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correct_stage = (CorrectCodeformerStage(), face_params, upscale_opts)
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elif "gfpgan" in upscale.correction_model:
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correct_stage = (CorrectGFPGANStage(), face_params, upscale_opts)
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else:
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logger.warn("unknown correction model: %s", upscale.correction_model)
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if upscale.upscale_order == "correction-both":
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chain.append(correct_stage)
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chain.append(upscale_stage)
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chain.append(correct_stage)
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elif upscale.upscale_order == "correction-first":
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chain.append(correct_stage)
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chain.append(upscale_stage)
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elif upscale.upscale_order == "correction-last":
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chain.append(upscale_stage)
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chain.append(correct_stage)
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else:
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logger.warn("unknown upscaling order: %s", upscale.upscale_order)
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if post_stages is not None:
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for stage, post_params, post_opts in post_stages:
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chain.append((stage, post_params, post_opts))
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return chain
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