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Fix behavior when encountering a bad embedding (#2721)

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When encountering a bad embedding, InvokeAI was asking about reconfiguring models. This is because the embedding load error was never handled - it now is.
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Jonathan 2023-02-19 08:04:59 -06:00 committed by GitHub
parent b9ecf93ba3
commit d3c1b747ee
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1 changed files with 5 additions and 1 deletions
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6
ldm/modules/textual_inversion_manager.py
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@ -79,7 +79,10 @@ class TextualInversionManager:
embedding_info = self._parse_embedding(str(ckpt_path)) embedding_info = self._parse_embedding(str(ckpt_path))
if ( if embedding_info is None:
# We've already put out an error message about the bad embedding in _parse_embedding, so just return.
return
elif (
self.text_encoder.get_input_embeddings().weight.data[0].shape[0] self.text_encoder.get_input_embeddings().weight.data[0].shape[0]
!= embedding_info["embedding"].shape[0] != embedding_info["embedding"].shape[0]
): ):
@ -287,6 +290,7 @@ class TextualInversionManager:
return self._parse_embedding_bin(embedding_file) return self._parse_embedding_bin(embedding_file)
else: else:
print(f">> Not a recognized embedding file: {embedding_file}") print(f">> Not a recognized embedding file: {embedding_file}")
return None
def _parse_embedding_pt(self, embedding_file): def _parse_embedding_pt(self, embedding_file):
embedding_ckpt = torch.load(embedding_file, map_location="cpu") embedding_ckpt = torch.load(embedding_file, map_location="cpu")