38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
import json
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import os
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import fcbh.sd
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def first_file(path, filenames):
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for f in filenames:
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p = os.path.join(path, f)
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if os.path.exists(p):
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return p
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return None
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def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None):
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diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"]
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unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names)
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vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names)
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text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"]
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text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names)
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text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names)
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text_encoder_paths = [text_encoder1_path]
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if text_encoder2_path is not None:
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text_encoder_paths.append(text_encoder2_path)
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unet = fcbh.sd.load_unet(unet_path)
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clip = None
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if output_clip:
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clip = fcbh.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory)
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vae = None
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if output_vae:
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sd = fcbh.utils.load_torch_file(vae_path)
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vae = fcbh.sd.VAE(sd=sd)
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return (unet, clip, vae)
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