CLIP with MLX — Adopting the advancement in multimodal learning

The field of machine learning has witnessed a surge of innovation in recent years, with models like CLIP (Contrastive Language-Image Pre-training) pushing the boundaries of multimodal learning. CLIP, developed by OpenAI, has revolutionized the way we understand and leverage the relationship between text and images. Now, with the introduction of MLX, Apple’s cutting-edge machine learning framework, the potential of CLIP has been taken to new heights.

In this article, we will explore how the combination of CLIP and MLX is set to transform the landscape of multimodal learning and open up exciting possibilities for researchers and developers alike.

Understanding CLIP

Before diving into the synergy between CLIP and MLX, let’s briefly recap what CLIP is and why it has garnered so much attention. CLIP is a neural network architecture that learns to associate text and images by jointly embedding them in a shared latent space. By training on a vast dataset of image-text pairs, CLIP learns to capture the semantic relationships between the two modalities, enabling tasks such as zero-shot image classification, image retrieval, and text-to-image synthesis.

The power of CLIP lies in its ability to bridge the gap between vision and language, allowing models to understand and reason about the world in a more human-like manner. With CLIP, we can ask questions like “Find an image of a cat sitting on a couch” and expect the model to retrieve relevant images based on its understanding of both the visual and textual concepts.

You can learn more in this article we published few months ago.

Apple’s MLX

While CLIP has already shown impressive results, the introduction of MLX takes its potential to new heights. MLX, Apple’s state-of-the-art machine learning framework designed specifically for Apple silicon, brings a host of benefits that enhance the performance, efficiency, and ease of use of CLIP models.

We also have an article for MLX here.

One of the key advantages of MLX is its seamless integration with the Apple ecosystem. With MLX, researchers and developers can harness the full power of Apple’s custom-designed chips, such as the A14 Bionic and M1, to accelerate the training and inference of CLIP models. The tight coupling between hardware and software enables optimal utilization of resources, resulting in faster and more efficient execution.

Moreover, MLX provides a user-friendly and intuitive API that simplifies the process of working with CLIP models. The framework offers high-level abstractions and pre-built components that encapsulate the complexities of training and deploying CLIP models, allowing researchers to focus on the core aspects of their work. With MLX, tasks like data preprocessing, model architecture definition, and hyperparameter tuning become more streamlined and accessible.

Another significant benefit of MLX is its extensive ecosystem of tools and libraries. The framework seamlessly integrates with popular machine learning libraries such as NumPy, PyTorch, and TensorFlow, enabling researchers to leverage existing codebases and workflows. Additionally, MLX provides a rich set of visualization and debugging tools that facilitate the analysis and interpretation of CLIP models, empowering researchers to gain deeper insights into their performance and behavior.

Pioneering new frontiers with CLIP and MLX

The combination of CLIP and MLX opens up a world of possibilities for multimodal learning. Let’s explore a few exciting applications and research directions:

1. Enhanced image search and retrieval: With CLIP and MLX, image search and retrieval systems can be taken to the next level. By leveraging the semantic understanding captured by CLIP, these systems can go beyond traditional keyword-based matching and understand the content and context of images at a deeper level. Users can pose natural language queries, and the system can retrieve the most relevant images based on their semantic similarity. This has profound implications for domains like e-commerce, digital asset management, and content discovery.
2. Intelligent image captioning and description: CLIP and MLX can revolutionize the way we generate captions and descriptions for images. By jointly embedding images and text, CLIP models can learn to generate accurate and contextually relevant captions for a wide range of images. This capability has applications in accessibility, where visually impaired individuals can benefit from automatic image descriptions, as well as in content creation and social media, where generating engaging captions can enhance user experiences.
3. Multimodal reasoning and question answering: The synergy between CLIP and MLX enables powerful multimodal reasoning and question-answering systems. By combining visual and textual information, these systems can answer complex questions that require an understanding of both modalities. For example, given an image and a question like “What is the color of the car parked next to the red building?”, a CLIP-based model can analyze the image, identify the relevant objects, and provide an accurate answer. This has applications in domains like autonomous vehicles, robotics, and intelligent assistants.
4. Creative content generation: CLIP and MLX can inspire new forms of creative content generation. By leveraging the semantic understanding captured by CLIP, generative models can create visually compelling and contextually relevant images based on textual prompts. Artists, designers, and content creators can explore novel ways of expression by combining the power of language and imagery. This opens up exciting possibilities for fields like digital art, advertising, and entertainment.

Getting started with CLIP and MLX

To showcase the seamless integration of CLIP and MLX, let’s walk through a concrete example from the MLX examples repository. This example demonstrates how to load a pre-trained CLIP model, preprocess images and text, and generate embeddings using MLX.

Step 1: Setup and Installation

To get started, clone the MLX examples repository and navigate to the CLIP example directory:

git clone https://github.com/ml-explore/mlx-examples.git
cd mlx-examples/clip

Next, install the required dependencies by running:

pip install -r requirements.txt

Step 2: Convert CLIP Model to MLX Format

Before using the CLIP model with MLX, we need to convert the pre-trained weights to the MLX format. In this example, we’ll use the openai/clip-vit-base-patch32 model from Hugging Face. Run the following command to download and convert the model:

python convert.py

This script will download the model and configuration files and save them in the mlx_model/ directory.

Step 3: Load CLIP Model and Preprocess Data

Now, let’s dive into the code to see how CLIP and MLX work together. Open the clip.py file and take a look at the following code snippet:

from PIL import Image
import clip

model, tokenizer, img_processor = clip.load("mlx_model")
inputs = {
    "input_ids": tokenizer(["a photo of a cat", "a photo of a dog"]),
    "pixel_values": img_processor(
        [Image.open("assets/cat.jpeg"), Image.open("assets/dog.jpeg")]
    ),
}

Here, we load the converted CLIP model, tokenizer, and image processor using the clip.load() function, specifying the path to the mlx_model/ directory.

Next, we prepare the input data by tokenizing the text descriptions using the tokenizer and preprocessing the images using the img_processor. The tokenizer converts the text into numerical representations (input_ids) that the model can understand, while the img_processor resizes, normalizes, and transforms the images into the required format.

Step 4: Generate embeddings

With the model loaded and data preprocessed, we can now generate embeddings for the text and images:

output = model(**inputs)

# Get text and image embeddings:
text_embeds = output.text_embeds
image_embeds = output.image_embeds

By passing the preprocessed inputs to the model, we obtain the output containing the text and image embeddings. These embeddings capture the semantic information and allow us to perform tasks like similarity comparison, retrieval, or classification.

Step 5: Run the example

To run the complete example, simply execute the following command:

python clip.py

This will load the CLIP model, preprocess the provided cat and dog images along with their text descriptions, and generate the corresponding embeddings.

Customization and extensions

The example in clip.py showcases a basic usage of CLIP with MLX, but you can easily customize and extend it to suit your specific needs. For example:

• To embed only images or only text, you can pass just the pixel_values or input_ids to the model, respectively.
• The example uses minimal image preprocessing and tokenization implemented in image_processor.py and tokenizer.py to reduce dependencies. However, you can leverage the transformers library for additional preprocessing functionality, as shown in hf_preproc.py.
• MLX CLIP has been tested and works with various Hugging Face model repositories, such as openai/clip-vit-base-patch32 and openai/clip-vit-large-patch14. You can experiment with different models by updating the MLX_PATH and HF_PATH variables in test.py.

By exploring the MLX examples repository and building upon the provided code, you can unlock the full potential of CLIP and MLX for your multimodal learning tasks.

Conclusion

The combination of CLIP and MLX represents a significant leap forward in multimodal learning. By leveraging the power of Apple silicon and the expressiveness of natural language, this synergy unlocks new frontiers in image understanding, retrieval, and generation. With MLX’s user-friendly API, optimized performance, and seamless integration, researchers and developers can push the boundaries of what is possible with CLIP models.

As we continue to explore the potential of CLIP and MLX, we can anticipate groundbreaking advancements in fields like computer vision, natural language processing, and artificial intelligence as a whole. The ability to bridge the gap between vision and language brings us closer to building intelligent systems that can perceive, reason, and interact with the world in more human-like ways.

Whether you are a researcher seeking to advance the state of the art in multimodal learning or a developer looking to incorporate cutting-edge capabilities into your applications, CLIP and MLX provide a powerful and accessible platform to realize your vision. So, embrace the potential of this game-changing combination and embark on a journey of innovation and discovery in the realm of multimodal learning.