ValueError: Number Of Image Placeholders In The Prompt Does Not Match The Number Of Images.

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Introduction

In this article, we will explore the issue of ValueError: Number of image placeholders in the prompt does not match the number of images. when using the transformers library for image-text-to-text tasks. We will go through the code examples provided and identify the root cause of the issue.

Code Examples

The first code example uses the pipeline function from the transformers library to create an image-text-to-text pipeline:

from transformers import pipeline

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "/data/xwz/vt2t/first_frame.png",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    },
]

pipe = pipeline("image-text-to-text", model="/data/xwz/tmp/InternVL3-14B-hf")
outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
outputs[0]["generated_text"]

The second code example uses the AutoProcessor and AutoModelForImageTextToText classes from the transformers library to create an image-text-to-text model:

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

torch_device = "cuda"
model_checkpoint = "/data/xwz/tmp/InternVL3-14B-hf"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
messages = [
    [
        {
            "role": "user",
            "content": [
                {"type": "image", "url": "/data/xwz/vt2t/first_frame.png"},
                {"type": "text", "text": "what is in this video?"},
            ],
        },
    ],
]

inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

output = model.generate(**inputs, max_new_tokens=25)

decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
decoded_outputs

Error Analysis

The error message ValueError: Number of image placeholders in the prompt does not match the number of images. indicates that the number of image placeholders in the prompt does not match the number of images provided.

In the first code example, the prompt contains a single image placeholder, but the messages list contains two images. This mismatch causes the error.

In the second code example, the prompt contains a single image placeholder, but the messages list contains two images. This mismatch causes the error.

Solution

To fix the issue, we need to ensure that the number of image placeholders in the prompt matches the number of images provided. We can do this by modifying the prompt to contain multiple image placeholders, or by modifying the messages list to contain a single image.

Here is an updated version of the first code example that fixes the issue:

from transformers import pipeline

messages = [
    {
        "role": "",
        "content": [
            {
                "type": "image",
                "image": "/data/xwz/vt2t/first_frame.png",
            },
            {
                "type": "image",
                "image": "/data/xwz/vt2t/second_frame.png",
            },
            {"type": "text", "text": "Describe these images."},
        ],
    },
]

pipe = pipeline("image-text-to-text", model="/data/xwz/tmp/InternVL3-14B-hf")
outputs = pipe(text=messages, max_new_tokens=50, return_full_text=False)
outputs[0]["generated_text"]

And here is an updated version of the second code example that fixes the issue:

from transformers import AutoProcessor, AutoModelForImageTextToText
import torch

torch_device = "cuda"
model_checkpoint = "/data/xwz/tmp/InternVL3-14B-hf"
processor = AutoProcessor.from_pretrained(model_checkpoint)
model = AutoModelForImageTextToText.from_pretrained(model_checkpoint, device_map=torch_device, torch_dtype=torch.bfloat16)
messages = [
    [
        {
            "role": "user",
            "content": [
                {"type": "image", "url": "/data/xwz/vt2t/first_frame.png"},
                {"type": "image", "url": "/data/xwz/vt2t/second_frame.png"},
                {"type": "text", "text": "what is in these videos?"},
            ],
        },
    ],
]

inputs = processor.apply_chat_template(messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(model.device, dtype=torch.bfloat16)

output = model.generate(**inputs, max_new_tokens=25)

decoded_outputs = processor.batch_decode(output, skip_special_tokens=True)
decoded_outputs

Conclusion

In this article, we explored the issue of ValueError: Number of image placeholders in the prompt does not match the number of images. when using the transformers library for image-text-to-text tasks. We identified the root cause of the issue and provided updated code examples that fix the issue. By ensuring that the number of image placeholders in the prompt matches the number of images provided, we can successfully run image-text-to-text tasks using the transformers library.

Q&A

Q: What is the error ValueError: Number of image placeholders in the prompt does not match the number of images.?

A: This error occurs when the number of image placeholders in the prompt does not match the number of images provided. This can happen when the prompt is not properly formatted or when the images are not correctly specified.

Q: Why do I get this error when using the transformers library?

A: The transformers library is designed to handle various types of text and image data. However, when working with image-text-to-text tasks, it's essential to ensure that the prompt and images are properly formatted. If the number of image placeholders in the prompt does not match the number of images provided, the library will raise a ValueError.

Q: How can I fix this error?

A: To fix this error, you need to ensure that the number of image placeholders in the prompt matches the number of images provided. You can do this by modifying the prompt to contain multiple image placeholders or by modifying the images list to contain a single image.

Q: What are some common mistakes that can cause this error?

A: Some common mistakes that can cause this error include:

  • Not properly formatting the prompt
  • Not correctly specifying the images
  • Not ensuring that the number of image placeholders in the prompt matches the number of images provided

Q: How can I prevent this error from occurring in the future?

A: To prevent this error from occurring in the future, make sure to:

  • Properly format the prompt
  • Correctly specify the images
  • Ensure that the number of image placeholders in the prompt matches the number of images provided

Q: What are some best practices for working with image-text-to-text tasks in the transformers library?

A: Some best practices for working with image-text-to-text tasks in the transformers library include:

  • Ensuring that the prompt and images are properly formatted
  • Correctly specifying the images
  • Ensuring that the number of image placeholders in the prompt matches the number of images provided
  • Using the apply_chat_template method to apply the chat template to the input data
  • Using the generate method to generate the output text

Q: Can I use the transformers library for other types of text and image data?

A: Yes, the transformers library can be used for other types of text and image data. However, you may need to modify the prompt and images to match the specific requirements of the task.

Q: Are there any other libraries that I can use for image-text-to-text tasks?

A: Yes, there are other libraries that you can use for image-text-to-text tasks, such as the torch library and the tensorflow library. However, the transformers library is a popular and widely-used choice for this type of task.

Q: Can I use the transformers library for other types of natural language processing tasks?

A: Yes, the transformers library can be used for other types of natural language processing tasks, such as text classification, sentiment analysis, and language translation. However, you may need to modify the prompt and images to match the specific requirements of the task.

Q: Are there any resources available for learning more about the transformers library and image-text-to-text tasks?

A: Yes, there are many resources available for learning more about the transformers library and image-text-to-text tasks, including the official documentation, tutorials, and online courses.