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text_encoder.py

@@ -154,7 +154,7 @@ class T5Embedder(torch.nn.Module):
         self.max_length = max_length
         dtype = torch.bfloat16
         self.dtype = dtype
-        t5_version = './t5-v1_1-xxl'
+        t5_version = 'google/t5-v1_1-xxl'
         self.t5_tokenizer = T5Tokenizer.from_pretrained(t5_version, max_length=max_length)
         self.t5_encoder = T5EncoderModel.from_pretrained(t5_version, torch_dtype=dtype).to(device=device)
         self.t5_encoder = self.t5_encoder.eval().requires_grad_(False)
@@ -214,7 +214,7 @@ class LoraT5EmbedderNoGradientCheck(torch.nn.Module):
         self.max_length = max_length
         dtype = torch.bfloat16
         self.dtype = dtype
-        t5_version = './t5-v1_1-xxl'
+        t5_version = 'google/t5-v1_1-xxl'
         self.t5_tokenizer = T5Tokenizer.from_pretrained(t5_version, max_length=max_length)
         self.t5_encoder = T5EncoderModel.from_pretrained(t5_version, torch_dtype=dtype).to(device=device).to(dtype)
         self.t5_encoder.gradient_checkpointing_enable()