To incorporate multi-modal inputs into a diffusion model, you need to condition the model on multiple data types (e.g., text, images, audio) during training and inference. This is typically done by embedding each modality into a shared latent space and using these embeddings to guide the denoising process. For example, text inputs can be processed by a language model like CLIP or BERT to generate text embeddings, while images might be encoded via a vision transformer. These embeddings are then combined—often using cross-attention layers—to influence how the diffusion model gradually removes noise from a random starting point. For instance, in text-to-image models like Stable Diffusion, cross-attention layers allow the model to align text prompts with visual features during generation. Similarly, audio inputs could be converted to spectrograms and embedded to influence the diffusion process for tasks like generating music synchronized with visual outputs.
Handling cross-modal alignment is critical. Modalities like text and images must be mapped to a shared representation so the diffusion model can understand their relationships. One approach is to train a joint embedding space using contrastive learning, where paired data (e.g., an image and its caption) are pulled closer in the embedding space. During training, the diffusion model learns to generate outputs that align with these combined embeddings. For example, a model trained on paired text and medical scans could use text descriptions to guide the generation of synthetic MRI images. Another method involves using adapters—small neural networks that project different modalities into a unified format. For instance, an audio-to-image model might use an adapter to convert mel-spectrograms into embeddings compatible with the diffusion model’s existing image encoder.
Practical implementation requires careful design choices. Training a multi-modal diffusion model often involves pre-trained encoders for efficiency. For example, using a frozen CLIP text encoder to handle text inputs reduces computational overhead. During inference, users can mix modalities flexibly: a model trained on text and sketches could generate images from either input alone or both combined. Challenges include balancing the influence of each modality—too much weight on text might ignore visual cues. Techniques like modality-specific loss weighting or dynamic gradient scaling during training can mitigate this. For example, in a video generation model using audio and text, the audio embedding’s contribution might be scaled higher for timing-sensitive frames. Testing with ablation studies (e.g., removing one modality) helps validate each input’s role in the final output.