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import torch
import gc
from collections import OrderedDict
from typing import TYPE_CHECKING
from jobs.process import BaseExtensionProcess
from toolkit.config_modules import ModelConfig
from toolkit.stable_diffusion_model import StableDiffusion
from toolkit.train_tools import get_torch_dtype
from tqdm import tqdm
# Type check imports. Prevents circular imports
if TYPE_CHECKING:
from jobs import ExtensionJob
# extend standard config classes to add weight
class ModelInputConfig(ModelConfig):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.weight = kwargs.get('weight', 1.0)
# overwrite default dtype unless user specifies otherwise
# float 32 will give up better precision on the merging functions
self.dtype: str = kwargs.get('dtype', 'float32')
def flush():
torch.cuda.empty_cache()
gc.collect()
# this is our main class process
class ExampleMergeModels(BaseExtensionProcess):
def __init__(
self,
process_id: int,
job: 'ExtensionJob',
config: OrderedDict
):
super().__init__(process_id, job, config)
# this is the setup process, do not do process intensive stuff here, just variable setup and
# checking requirements. This is called before the run() function
# no loading models or anything like that, it is just for setting up the process
# all of your process intensive stuff should be done in the run() function
# config will have everything from the process item in the config file
# convince methods exist on BaseProcess to get config values
# if required is set to true and the value is not found it will throw an error
# you can pass a default value to get_conf() as well if it was not in the config file
# as well as a type to cast the value to
self.save_path = self.get_conf('save_path', required=True)
self.save_dtype = self.get_conf('save_dtype', default='float16', as_type=get_torch_dtype)
self.device = self.get_conf('device', default='cpu', as_type=torch.device)
# build models to merge list
models_to_merge = self.get_conf('models_to_merge', required=True, as_type=list)
# build list of ModelInputConfig objects. I find it is a good idea to make a class for each config
# this way you can add methods to it and it is easier to read and code. There are a lot of
# inbuilt config classes located in toolkit.config_modules as well
self.models_to_merge = [ModelInputConfig(**model) for model in models_to_merge]
# setup is complete. Don't load anything else here, just setup variables and stuff
# this is the entire run process be sure to call super().run() first
def run(self):
# always call first
super().run()
print(f"Running process: {self.__class__.__name__}")
# let's adjust our weights first to normalize them so the total is 1.0
total_weight = sum([model.weight for model in self.models_to_merge])
weight_adjust = 1.0 / total_weight
for model in self.models_to_merge:
model.weight *= weight_adjust
output_model: StableDiffusion = None
# let's do the merge, it is a good idea to use tqdm to show progress
for model_config in tqdm(self.models_to_merge, desc="Merging models"):
# setup model class with our helper class
sd_model = StableDiffusion(
device=self.device,
model_config=model_config,
dtype="float32"
)
# load the model
sd_model.load_model()
# adjust the weight of the text encoder
if isinstance(sd_model.text_encoder, list):
# sdxl model
for text_encoder in sd_model.text_encoder:
for key, value in text_encoder.state_dict().items():
value *= model_config.weight
else:
# normal model
for key, value in sd_model.text_encoder.state_dict().items():
value *= model_config.weight
# adjust the weights of the unet
for key, value in sd_model.unet.state_dict().items():
value *= model_config.weight
if output_model is None:
# use this one as the base
output_model = sd_model
else:
# merge the models
# text encoder
if isinstance(output_model.text_encoder, list):
# sdxl model
for i, text_encoder in enumerate(output_model.text_encoder):
for key, value in text_encoder.state_dict().items():
value += sd_model.text_encoder[i].state_dict()[key]
else:
# normal model
for key, value in output_model.text_encoder.state_dict().items():
value += sd_model.text_encoder.state_dict()[key]
# unet
for key, value in output_model.unet.state_dict().items():
value += sd_model.unet.state_dict()[key]
# remove the model to free memory
del sd_model
flush()
# merge loop is done, let's save the model
print(f"Saving merged model to {self.save_path}")
output_model.save(self.save_path, meta=self.meta, save_dtype=self.save_dtype)
print(f"Saved merged model to {self.save_path}")
# do cleanup here
del output_model
flush()
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