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modeling_llavanext_for_embedding.py

@@ -305,7 +305,6 @@ class LLaVANextForEmbedding(LlavaNextForConditionalGeneration):
             text_input = self.prepare_text_input(images, text, q_or_c, task_instruction)
             text_input = [text_input]
             
-            print(text_input)
 
             if images is not None:
                 images = Image.open(images).resize((512,512)).convert("RGB")
@@ -315,10 +314,10 @@ class LLaVANextForEmbedding(LlavaNextForConditionalGeneration):
                 inputs = self.processor(text=text_input, return_tensors="pt", padding=True)
 
         else:
+            if text is None:
+                text = [None] * len(images)
             text_input = [self.prepare_text_input(_image, _text, q_or_c, task_instruction) for _image, _text in zip(images, text)]
             
-            print(text_input)
-
             if images is not None:
                 images = [Image.open(_image).resize((512,512)).convert("RGB") for _image in images]
                 inputs = self.processor(images=images, text=text_input, return_tensors="pt", padding=True)