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https://github.com/invoke-ai/InvokeAI
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Merge branch 'main' of github.com:lstein/stable-diffusion into main
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commit
08a9702b73
@ -215,11 +215,13 @@ class EmbeddingManager(nn.Module):
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ckpt_path,
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)
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def load(self, ckpt_path):
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def load(self, ckpt_path, full=True):
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ckpt = torch.load(ckpt_path, map_location='cpu')
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self.string_to_token_dict = ckpt['string_to_token']
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self.string_to_param_dict = ckpt['string_to_param']
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self.string_to_token_dict = ckpt["string_to_token"]
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self.string_to_param_dict = ckpt["string_to_param"]
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if not full:
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for key, value in self.string_to_param_dict.items():
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self.string_to_param_dict[key] = torch.nn.Parameter(value.half())
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def get_embedding_norms_squared(self):
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all_params = torch.cat(
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@ -498,7 +498,7 @@ class T2I:
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self.device = self._get_device()
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model = self._load_model_from_config(config, self.weights)
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if self.embedding_path is not None:
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model.embedding_manager.load(self.embedding_path)
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model.embedding_manager.load(self.embedding_path, self.full_precision)
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self.model = model.to(self.device)
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# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
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self.model.cond_stage_model.device = self.device
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