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IzumiSatoshi/main.py

Created January 28, 2023 16:37
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diffusers形式のInstructPix2Pixを通常モデルとマージ
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main.py
# 動作未確認
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler, StableDiffusionPipeline
import gc
# load models
ip2p_pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained("/content/drive/MyDrive/models/diffusers/ip2p", torch_dtype=torch.float16)
base_pipe = StableDiffusionPipeline.from_pretrained("/content/drive/MyDrive/models/diffusers/sd15_fp16", torch_dtype=torch.float16)
target_pipe = StableDiffusionPipeline.from_pretrained("/content/drive/MyDrive/models/diffusers/dedede_fp16", torch_dtype=torch.float16)
# get state dict
ip2p_dict = ip2p_pipe.unet.state_dict()
base_dict = base_pipe.unet.state_dict()
target_dict = target_pipe.unet.state_dict()
# merged = ip2p + target - base
for key in ip2p_dict.keys():
# skip conv_in
if key == "conv_in.weight":
print("pass")
else:
ip2p_dict[key] = ip2p_dict[key] + target_dict[key] - base_dict[key]
# save コンフィグファイルが元のip2pのままなので、何か不具合が出るかもしれない
ip2p_pipe.unet.load_state_dict(ip2p_dict)
ip2p_pipe.vae = target_pipe.vae
ip2p_pipe.save_pretrained("/content/drive/MyDrive/models/diffusers/dedede_ip2p", safe_serialization=True)
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