90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
import logging as logger
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from .architecture.face.codeformer import CodeFormer
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from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean
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from .architecture.face.restoreformer_arch import RestoreFormer
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from .architecture.HAT import HAT
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from .architecture.LaMa import LaMa
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from .architecture.MAT import MAT
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from .architecture.RRDB import RRDBNet as ESRGAN
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from .architecture.SPSR import SPSRNet as SPSR
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from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
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from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
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from .architecture.Swin2SR import Swin2SR
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from .architecture.SwinIR import SwinIR
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from .types import PyTorchModel
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class UnsupportedModel(Exception):
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pass
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def load_state_dict(state_dict) -> PyTorchModel:
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logger.debug(f"Loading state dict into pytorch model arch")
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state_dict_keys = list(state_dict.keys())
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if "params_ema" in state_dict_keys:
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state_dict = state_dict["params_ema"]
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elif "params-ema" in state_dict_keys:
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state_dict = state_dict["params-ema"]
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elif "params" in state_dict_keys:
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state_dict = state_dict["params"]
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state_dict_keys = list(state_dict.keys())
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# SRVGGNet Real-ESRGAN (v2)
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if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys:
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model = RealESRGANv2(state_dict)
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# SPSR (ESRGAN with lots of extra layers)
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elif "f_HR_conv1.0.weight" in state_dict:
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model = SPSR(state_dict)
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# Swift-SRGAN
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elif (
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"model" in state_dict_keys
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and "initial.cnn.depthwise.weight" in state_dict["model"].keys()
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):
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model = SwiftSRGAN(state_dict)
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# HAT -- be sure it is above swinir
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elif "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" in state_dict_keys:
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model = HAT(state_dict)
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# SwinIR
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elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys:
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if "patch_embed.proj.weight" in state_dict_keys:
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model = Swin2SR(state_dict)
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else:
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model = SwinIR(state_dict)
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# GFPGAN
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elif (
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"toRGB.0.weight" in state_dict_keys
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and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys
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):
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model = GFPGANv1Clean(state_dict)
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# RestoreFormer
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elif (
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"encoder.conv_in.weight" in state_dict_keys
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and "encoder.down.0.block.0.norm1.weight" in state_dict_keys
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):
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model = RestoreFormer(state_dict)
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elif (
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"encoder.blocks.0.weight" in state_dict_keys
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and "quantize.embedding.weight" in state_dict_keys
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):
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model = CodeFormer(state_dict)
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# LaMa
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elif (
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"model.model.1.bn_l.running_mean" in state_dict_keys
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or "generator.model.1.bn_l.running_mean" in state_dict_keys
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):
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model = LaMa(state_dict)
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# MAT
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elif "synthesis.first_stage.conv_first.conv.resample_filter" in state_dict_keys:
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model = MAT(state_dict)
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# Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1
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else:
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try:
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model = ESRGAN(state_dict)
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except:
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# pylint: disable=raise-missing-from
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raise UnsupportedModel
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return model
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