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support for sd-concepts as alternatives for textual inversion #151
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@ -9,4 +9,5 @@ __pycache__
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/outputs
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/config.json
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/log
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webui.settings.bat
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/webui.settings.bat
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/embeddings
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@ -73,11 +73,21 @@ class StableDiffusionModelHijack:
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name = os.path.splitext(filename)[0]
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data = torch.load(path)
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param_dict = data['string_to_param']
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if hasattr(param_dict, '_parameters'):
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param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
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assert len(param_dict) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(param_dict.items()))[1]
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# textual inversion embeddings
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if 'string_to_param' in data:
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param_dict = data['string_to_param']
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if hasattr(param_dict, '_parameters'):
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param_dict = getattr(param_dict, '_parameters') # fix for torch 1.12.1 loading saved file from torch 1.11
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assert len(param_dict) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(param_dict.items()))[1]
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elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor:
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assert len(data.keys()) == 1, 'embedding file has multiple terms in it'
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emb = next(iter(data.values()))
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if len(emb.shape) == 1:
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emb = emb.unsqueeze(0)
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self.word_embeddings[name] = emb.detach()
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self.word_embeddings_checksums[name] = f'{const_hash(emb.reshape(-1))&0xffff:04x}'
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