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https://github.com/invoke-ai/InvokeAI
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reduced verbosity of embed loading messages
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@ -964,6 +964,7 @@ class Generate:
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seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
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seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
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if self.embedding_path is not None:
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if self.embedding_path is not None:
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print(f'>> Loading embeddings from {self.embedding_path}')
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for root, _, files in os.walk(self.embedding_path):
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for root, _, files in os.walk(self.embedding_path):
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for name in files:
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for name in files:
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ti_path = os.path.join(root, name)
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ti_path = os.path.join(root, name)
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@ -971,7 +972,7 @@ class Generate:
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ti_path, defer_injecting_tokens=True
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ti_path, defer_injecting_tokens=True
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)
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)
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print(
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print(
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f'>> Textual inversions available: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}'
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f'>> Textual inversion triggers: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}'
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)
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)
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self.model_name = model_name
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self.model_name = model_name
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@ -69,7 +69,6 @@ class TextualInversionManager:
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return
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return
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try:
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try:
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print(f'>> Scanning {str(ckpt_path)} for embedding terms')
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scan_result = scan_file_path(str(ckpt_path))
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scan_result = scan_file_path(str(ckpt_path))
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if scan_result.infected_files == 1:
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if scan_result.infected_files == 1:
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print(
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print(
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