Integrating external knowledge bases into a diffusion framework involves modifying the model’s architecture or sampling process to leverage structured or unstructured data from external sources. One common approach is to condition the diffusion model on knowledge embeddings. For example, a text-to-image diffusion model could use structured data from a knowledge graph (e.g., entity relationships or attributes) as additional input. This is often done by encoding the knowledge into embeddings and integrating them into the model’s cross-attention layers. For instance, if generating an image of a “19th-century steam engine,” the model could retrieve engineering schematics or material properties from a knowledge base to improve accuracy in details like wheel design or boiler placement.
Another method is retrieval-augmented diffusion, where the model dynamically queries a knowledge base during the sampling process. At each denoising step, the model might fetch relevant information (e.g., textual descriptions, images, or metadata) to guide the generation. For example, a medical imaging diffusion model could retrieve patient-specific data from electronic health records to generate synthetic scans that reflect a patient’s unique anatomy. This requires a robust retrieval system (like a vector database) to index and fetch contextually relevant knowledge efficiently. The retrieved data is then fused into the diffusion process, either by concatenating it with latent features or using it to modulate the denoising network’s weights.
Lastly, knowledge bases can be used post-generation for refinement. After an initial output is created, a separate verification model could cross-check it against the knowledge base to identify inconsistencies. For example, a diffusion model generating historical scenes might produce an image with anachronistic clothing; a classifier trained on historical data could flag this, triggering a refinement step. While less integrated than the previous methods, this approach is simpler to implement and works with existing models. However, it adds computational overhead and may not correct errors as effectively as methods embedded in the diffusion loop. Developers should prioritize methods based on their use case’s latency and accuracy requirements.