add truncation when > max_length

library/lumina_train_util.py

@@ -320,7 +320,6 @@ def sample_image_inference(
 
         # Load sample prompts from Gemma 2
         if gemma2_model is not None:
-            logger.info(f"Encoding prompt with Gemma2: {prompt}")
             tokens_and_masks = tokenize_strategy.tokenize(prompt)
             gemma2_conds = encoding_strategy.encode_tokens(tokenize_strategy, [gemma2_model], tokens_and_masks)
 

library/strategy_lumina.py

@@ -54,6 +54,7 @@ class LuminaTokenizeStrategy(TokenizeStrategy):
             max_length=self.max_length,
             return_tensors="pt",
             padding="max_length",
+            truncation=True,
             pad_to_multiple_of=8,
         )
         return (encodings.input_ids, encodings.attention_mask)