How can user-guided generation be implemented in diffusion models?

User-guided generation in diffusion models can be implemented by integrating user inputs into the sampling process, modifying the model’s behavior through conditioning, or adjusting intermediate outputs during denoising. This typically involves designing interfaces or control mechanisms that allow users to influence the model’s predictions at specific stages. For example, users might provide sketches, text prompts, or spatial constraints to steer the model toward desired outputs. The key is to balance user input with the model’s generative capabilities, ensuring guidance is applied without overly restricting creativity.

One common approach is conditional diffusion, where user inputs are encoded as additional model inputs. For instance, in text-to-image models like Stable Diffusion, text embeddings are concatenated with latent representations at each denoising step. Developers can extend this by adding custom conditioning signals, such as segmentation masks or edge maps. For example, ControlNet creates a trainable copy of a diffusion model’s weights, allowing it to process auxiliary inputs (e.g., sketches) alongside the original latent space. During training, the model learns to align these inputs with target outputs, enabling real-time guidance during inference. This requires modifying the sampling loop to incorporate the user’s input at every step, often through cross-attention layers or concatenation.

Another method involves interactive sampling, where users adjust intermediate outputs during generation. For example, a developer could build a tool that lets users modify the latent space or gradients mid-sampling. Techniques like “guidance scale” tuning—where a parameter controls the strength of conditioning—can be exposed as adjustable sliders. Inpainting models demonstrate this by allowing users to mask regions and provide text prompts for specific areas. Implementing this requires modifying the diffusion process to accept partial updates (e.g., freezing unmasked regions) and re-injecting user feedback iteratively. Libraries like Diffusers provide APIs for such use cases, letting developers hook into denoising steps to apply custom logic. Challenges include maintaining coherence between user edits and the model’s predictions, which often requires careful balancing of loss terms or regularization during training.