  Conditional generative models typically demand large annotated training sets
to achieve high-quality synthesis. As a result, there has been significant
interest in designing models that perform plug-and-play generation, i.e., to
use a predefined or pretrained model, which is not explicitly trained on the
generative task, to guide the generative process (e.g., using language).
However, such guidance is typically useful only towards synthesizing high-level
semantics rather than editing fine-grained details as in image-to-image
translation tasks. To this end, and capitalizing on the powerful fine-grained
generative control offered by the recent diffusion-based generative models, we
introduce Steered Diffusion, a generalized framework for photorealistic
zero-shot conditional image generation using a diffusion model trained for
unconditional generation. The key idea is to steer the image generation of the
diffusion model at inference time via designing a loss using a pre-trained
inverse model that characterizes the conditional task. This loss modulates the
sampling trajectory of the diffusion process. Our framework allows for easy
incorporation of multiple conditions during inference. We present experiments
using steered diffusion on several tasks including inpainting, colorization,
text-guided semantic editing, and image super-resolution. Our results
demonstrate clear qualitative and quantitative improvements over
state-of-the-art diffusion-based plug-and-play models while adding negligible
additional computational cost.
