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88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
import os
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from collections import OrderedDict
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from safetensors.torch import save_file
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from jobs.process.BaseProcess import BaseProcess
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from toolkit.metadata import get_meta_for_safetensors
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from typing import ForwardRef
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from toolkit.train_tools import get_torch_dtype
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class BaseExtractProcess(BaseProcess):
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process_id: int
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config: OrderedDict
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output_folder: str
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output_filename: str
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output_path: str
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def __init__(
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self,
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process_id: int,
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job,
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config: OrderedDict
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):
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super().__init__(process_id, job, config)
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self.process_id = process_id
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self.job = job
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self.config = config
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self.dtype = self.get_conf('dtype', self.job.dtype)
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self.torch_dtype = get_torch_dtype(self.dtype)
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self.extract_unet = self.get_conf('extract_unet', self.job.extract_unet)
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self.extract_text_encoder = self.get_conf('extract_text_encoder', self.job.extract_text_encoder)
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def run(self):
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# here instead of init because child init needs to go first
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self.output_path = self.get_output_path()
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# implement in child class
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# be sure to call super().run() first
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pass
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# you can override this in the child class if you want
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# call super().get_output_path(prefix="your_prefix_", suffix="_your_suffix") to extend this
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def get_output_path(self, prefix=None, suffix=None):
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config_output_path = self.get_conf('output_path', None)
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config_filename = self.get_conf('filename', None)
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# replace [name] with name
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if config_output_path is not None:
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config_output_path = config_output_path.replace('[name]', self.job.name)
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return config_output_path
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if config_output_path is None and config_filename is not None:
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# build the output path from the output folder and filename
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return os.path.join(self.job.output_folder, config_filename)
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# build our own
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if suffix is None:
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# we will just add process it to the end of the filename if there is more than one process
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# and no other suffix was given
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suffix = f"_{self.process_id}" if len(self.config['process']) > 1 else ''
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if prefix is None:
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prefix = ''
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output_filename = f"{prefix}{self.output_filename}{suffix}"
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return os.path.join(self.job.output_folder, output_filename)
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def save(self, state_dict):
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# prepare meta
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save_meta = get_meta_for_safetensors(self.meta, self.job.name)
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# save
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os.makedirs(os.path.dirname(self.output_path), exist_ok=True)
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for key in list(state_dict.keys()):
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v = state_dict[key]
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v = v.detach().clone().to("cpu").to(self.torch_dtype)
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state_dict[key] = v
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# having issues with meta
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save_file(state_dict, self.output_path, save_meta)
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print(f"Saved to {self.output_path}")
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