Llava: generate without images (#32183)

* llava w/o images

* tests

src/transformers/models/llava/processing_llava.py

@@ -104,14 +104,14 @@ class LlavaProcessor(ProcessorMixin):
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
         """
         if images is not None:
-            pixel_values = self.image_processor(images, return_tensors=return_tensors)["pixel_values"]
+            image_inputs = self.image_processor(images, return_tensors=return_tensors)
         else:
-            pixel_values = None
+            image_inputs = {}
         text_inputs = self.tokenizer(
             text, return_tensors=return_tensors, padding=padding, truncation=truncation, max_length=max_length
         )
 
-        return BatchFeature(data={**text_inputs, "pixel_values": pixel_values})
+        return BatchFeature(data={**text_inputs, **image_inputs})
 
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
     def batch_decode(self, *args, **kwargs):

tests/models/llava/test_modeling_llava.py

@@ -458,3 +458,16 @@ class LlavaForConditionalGenerationIntegrationTest(unittest.TestCase):
         EXPECTED_OUTPUT = ['<|im_start|>', 'system', '\n', 'Answer', '▁the', '▁questions', '.', '<|im_end|>', '<|im_start|>', 'user', '\n', '<image>', '\n', 'What', '▁is', '▁shown', '▁in', '▁this', '▁image', '?', '<|im_end|>', '<|im_start|>', 'ass', 'istant', '\n']  # fmt: skip
         self.assertEqual(slow_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)
         self.assertEqual(fast_tokenizer.tokenize(prompt), EXPECTED_OUTPUT)
+
+    @slow
+    @require_bitsandbytes
+    def test_generation_no_images(self):
+        model_id = "llava-hf/llava-1.5-7b-hf"
+        model = LlavaForConditionalGeneration.from_pretrained(model_id, load_in_4bit=True)
+        processor = AutoProcessor.from_pretrained(model_id)
+
+        # Prepare inputs with no images
+        inputs = processor("Hello, I am", return_tensors="pt").to(torch_device)
+
+        # Make sure that `generate` works
+        _ = model.generate(**inputs, max_new_tokens=20)