How can you handle multi-modal input data when training generative models for text and image synthesis

Question

Can you tell me How can you handle multi-modal input data when training generative models for text and image synthesis?

Answer 1

Multi-modal input data can be handled by aligning shared representations of different data types (like text and images) in a common latent space, enabling cross-modal generation and synthesis.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Uses the CLIP model for multi-modal learning, aligning text and image representations.
• Processes both text and image inputs and converts them into embeddings.
• Measures similarity between text and image embeddings for cross-modal understanding.

Hence, using a shared latent space for text and image embeddings enables effective cross-modal learning and synthesis, as demonstrated by the CLIP model.