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support full-precision embeddings in half precision mode

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This commit is contained in:
Paul Sajna 2022-08-26 08:30:58 -07:00
parent 8b682ac83b
commit 9b5101cd8d
2 changed files with 6 additions and 4 deletions
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8
ldm/modules/embedding_manager.py
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@ -135,11 +135,13 @@ class EmbeddingManager(nn.Module):
torch.save({"string_to_token": self.string_to_token_dict, torch.save({"string_to_token": self.string_to_token_dict,
"string_to_param": self.string_to_param_dict}, ckpt_path) "string_to_param": self.string_to_param_dict}, ckpt_path)
def load(self, ckpt_path): def load(self, ckpt_path, full=True):
ckpt = torch.load(ckpt_path, map_location='cpu') ckpt = torch.load(ckpt_path, map_location='cpu')
self.string_to_token_dict = ckpt["string_to_token"] self.string_to_token_dict = ckpt["string_to_token"]
self.string_to_param_dict = ckpt["string_to_param"] self.string_to_param_dict = ckpt["string_to_param"]
if not full:
for key, value in self.string_to_param_dict.items():
self.string_to_param_dict[key] = torch.nn.Parameter(value.half())
def get_embedding_norms_squared(self): def get_embedding_norms_squared(self):
all_params = torch.cat(list(self.string_to_param_dict.values()), axis=0) # num_placeholders x embedding_dim all_params = torch.cat(list(self.string_to_param_dict.values()), axis=0) # num_placeholders x embedding_dim
@ -161,4 +163,4 @@ class EmbeddingManager(nn.Module):
loss = loss + (optimized - coarse) @ (optimized - coarse).T / num_embeddings loss = loss + (optimized - coarse) @ (optimized - coarse).T / num_embeddings
return loss return loss

2
ldm/simplet2i.py
View File

@ -454,7 +454,7 @@ The vast majority of these arguments default to reasonable values.
self.device = torch.device(self.device) if torch.cuda.is_available() else torch.device("cpu") self.device = torch.device(self.device) if torch.cuda.is_available() else torch.device("cpu")
model = self._load_model_from_config(config,self.weights) model = self._load_model_from_config(config,self.weights)
if self.embedding_path is not None: if self.embedding_path is not None:
model.embedding_manager.load(self.embedding_path) model.embedding_manager.load(self.embedding_path, self.full_precision)
self.model = model.to(self.device) self.model = model.to(self.device)
# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
self.model.cond_stage_model.device = self.device self.model.cond_stage_model.device = self.device